Processing Biases 1
Running Head: FACIAL AFFECT PROCESSING BIASES
This article may not exactly replicate the final version published in the Psychological Bulletin. It
is not the copy of record, which is copyrighted by the American Psychological Association.
© 2011 American Psychological Association
Facial Affect Processing and Depression Susceptibility: Cognitive Biases and Cognitive
Neuroscience
Steven L. Bistricky
University of California, San Francisco
Rick E. Ingram and Ruth Ann Atchley
University of Kansas
Author Note
Steven L. Bistricky, University of California San Francisco; Rick E. Ingram, University
of Kansas; Ruth Ann Atchley, University of Kansas.
We would like to thank Stephen Ilardi for his helpful comments on an early version of
this article.
Correspondence concerning this article should be addressed to Steven L. Bistricky,
Department of Psychiatry, University of California San Francisco, 401 Parnassus Ave. Box 0984
– CPT, San Francisco, CA 94143. Email: [email protected]
Processing Biases 2
Abstract
Facial affect processing is essential to social development and functioning, and is particularly
relevant to models of depression. Although cognitive and interpersonal theories have long
described different pathways to depression, cognitive-interpersonal and evolutionary social risk
models of depression focus on the interrelation of interpersonal experience, cognition, and social
behavior. We therefore review the burgeoning depressive facial affect processing literature and
examine its potential for integrating disciplines, theories, and research. In particular, we evaluate
studies that used information processing or cognitive neuroscience paradigms to assess facial
affect processing in depressed and depression-susceptible populations. Most studies have
assessed and supported cognitive models. This research suggests that depressed and depression
vulnerable groups show abnormal facial affect interpretation, attention, and memory, although
findings vary based on depression severity, comorbid anxiety, or length of time faces are viewed.
Facial affect processing biases appear to correspond with distinct neural activity patterns and
increased depressive emotion and thought. Biases typically emerge in depressed moods, but are
occasionally found in the absence of such moods. Indirect evidence suggests that childhood
neglect might cultivate abnormal facial affect processing, which can impede social functioning in
ways consistent with cognitive-interpersonal and interpersonal models. However, reviewed
studies provide mixed support for the social risk model prediction that depressive states prompt
cognitive hypervigilance to social threat information. Based on the current literature, we
recommend prospective interdisciplinary research examining whether facial affect processing
abnormalities promote—or are promoted by—depressogenic attachment experiences, negative
thinking, and social dysfunction.
Keywords: Depression, Vulnerability, Bias, Facial Affect, Neuroscience
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Facial Affect Processing and Depression Susceptibility: Cognitive Biases and Cognitive
Neuroscience
Major depression is a leading cause of disability worldwide (World Health Organization,
2006), and it is associated with impaired interpersonal, cognitive, occupational, and health
functioning. Multiple causal pathways can lead to depression, but etiological mechanisms are not
fully-understood (Hammen, Bistricky, & Ingram, 2009). Nonetheless, evaluating current
evidence for processes theorized to promote depression can increase understanding of the
disorder and inform intervention strategies.
Historically, independent theoretical contingents have proposed that either cognitive
factors or interpersonal factors promote depression (e.g., Beck, 1967; Coyne, 1976; Lewinsohn,
1974). More recently, two noteworthy attempts have been made to integrate these theoretical
literatures. Gotlib and Hammen (1992) proposed a cognitive-interpersonal model, and Allen and
Badcock (2003) forwarded an evolutionary social risk hypothesis. To varying degrees these four
models implicate abnormal social behavior and cognition.
Most studies investigating emotional information processing in depressive populations
have used lexical stimuli. However, a recent wave of studies has shown integrative promise by
examining processing of social emotional information—facial affect. Most of these studies have
been guided by cognitive approaches to depression, which have traditionally laid claim to
emotional information processing. Finding convergence or discrepancy between verbal-semantic
and facial affective processing modalities would help refine cognitive models. However, basic
facial affect processing biases might also influence complex depressotypic cognition, behavior,
and interaction patterns, processes we review with interpersonal, cognitive-interpersonal, and
social risk theories. Because these theories tend to be more complementary than contradictory,
Processing Biases 4
confirming reliable facial affect processing biases in depression susceptible samples would
encourage more direct examination of possible relationships between proposed depressogenic
cognitive and interpersonal factors. This review highlights a conceptual blueprint and the
empirical foundation of a bridge joining theoretical perspectives, which will help frame our
understanding of depression vulnerability and promote interdisciplinary ―bench science‖.
To appreciate the important relationship between facial affect processing and depression,
one must understand the roles of affect perception in normal human functioning. At a
psychological level, facial affect processing is instrumental in social development, emotion
regulation, and social functioning (Cozolino, 2002; Leppӓnen & Hietanen, 2001). At a
neurological level, facial affect is processed by specialized networks within a particular circuit of
brain structures, some of which function abnormally in depression (Surguladze et al., 2005).
Thus, humans appear programmed to make use of facial affect information.
Several additional conceptual factors impel a separate, systematic review of research to
evaluate depressive processing of facial affect. First, facial affect effectively transmits and
evokes emotions (Ruys & Stapel, 2008). That is, facial affect simultaneously indicates the tenor
of the present social situation and influences how one feels. Thus, an attentional bias toward sad
facial expressions might result in a disproportionately depressive mood and mental
representation of the social environment. Second, direct gaze facial affect—which most studies
use—can initiate automatic self-referent processing (i.e., ―he is looking at me‖) and self-relative-
to-other processing (e.g., ―is his response to me unfavorable and dominant?‖). In this way, facial
expressions may tap the looking glass self, which is continually molded by the reflections of
others. Some evidence suggests that for depressed individuals, viewing others’ affect triggers
negative self evaluation and assumptions of others’ harsh judgments (Frewen & Dozois, 2005).
Processing Biases 5
Third, inasmuch as emotions are preparatory states for action, perceiving facial affect should
prime an immediate, appropriate social reaction. By comparison, language is often more abstract
and detached from present time and place. In line with this conceptual difference, cognitive
neuroscience suggests that facial affect may be a more evocative medium of transmitting
emotion than words (Vanderploeg, Brown, & Marsh, 1987). Fourth, facial affect helps establish
parameters of emotion processing and regulation networks before left-hemisphere-mediated
linguistic and narrative interpretation capabilities are well-developed (Chapman, 2000; Cozolino,
2002; Gazzaniga, 1989). This preverbal foundation of implicit affective memories forms an
enduring basis for intuitions about others and the world even after one can describe, rationalize,
and explicitly remember events linked to pleasant or unpleasant emotional experience.
Fifth, facial affect represents particularly salient information to depression-prone
individuals negotiating social environments. Depressed individuals commonly display impaired
interpersonal functioning, which correlates with facial affect decoding ability (Leppӓnen &
Hietanen, 2001). Lastly, facial affect can be dynamic and involuntary. As a result, it is a more
trusted indicator of a person’s internal emotions or attitudes toward an interaction partner than
more consciously controlled verbal content. Thus, a depressed person hearing supportive
platitudes might instead focus on others’ micro-expressions of ambivalence or frustration. Based
on similar reasoning, commentators have argued that depressive information biases should be
pronounced for affective interpersonal information (Allen & Badcock, 2003; Gotlib,
Krasnoperova, Yue, & Joormann, 2004). For all these reasons, facial affect is not arbitrarily
interchangeable with words as a modality of valence. A separate evaluation of depressive facial
affect processing is needed.
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The growing collection of facial affect information processing studies has not been
reviewed and evaluated based on cognitive, interpersonal, cognitive-interpersonal, and social risk
theories of depression. Additionally, cognitive neuroscience has begun to reveal depressive
attention, interpretation, and memory bias findings for affective facial stimuli. These findings
have also not been reviewed and reconciled with germane theories of depression.
The present review has two broad goals. Our main purpose is to provide a systematic
review of empirical studies that examine facial affect processing in depression susceptible
groups. We focus on the larger collection of studies employing traditional information
processing approaches and incorporate pertinent cognitive neuroscience findings. Cognitive
neuroscience can illuminate neural processes that might underlie depressive information
processing. Our second goal is to evaluate the fit of the empirical literature with cognitive,
interpersonal, cognitive-interpersonal, and social risk frameworks of depression. Cognitive
theory can be more easily evaluated using current empirical evidence than other theories.
Therefore, we focus on cognitive theory but address the potential relevance of the empirical
findings to the other three theories.
To contextualize and justify the goals of the review, we introduce relevant topics and
issues. First, we establish the theoretical importance of studying depression susceptibility and
facial affect processing. We begin by reviewing important conceptual and methodological issues
related to studying depression vulnerability. Next, we describe the cognitive, interpersonal,
cognitive-interpersonal, and social risk models of depression, focusing on aspects most relevant
to facial affect processing. We continue by discussing the importance of facial affect processing
in normal human social development, social function, survival, and evolution. We also review
the neural underpinnings of facial affect processing and evidence of differential processing
Processing Biases 7
activity in depression. Next, because various tasks can assess different cognitive processing
stages and elicit nuanced findings, we describe information processing tasks and neuroscience
techniques used in the literature. We then explain the method used for the present review.
Finally, we review and critically evaluate empirical studies that have examined facial affect
processing in depressed or depression-susceptible populations. We conclude with a synthesis of
empirical findings and theoretical models, and we identify areas in need of future research.
Important Theoretical and Methodological Issues in the Literature
Studying depression and depression vulnerability. Although individuals are directly
classified as depressed by meeting diagnostic criteria, operationalizing depression vulnerability
is necessarily indirect. Studies operationalize vulnerability according to known risk factors. For
example, vulnerable groups typically have experienced a past major depressive episode, have
parents with a history of depression, or are experiencing stable dysphoria, a subclinical syndrome
that includes depressive and anxious symptoms (Ingram & Hamilton, 1999). Researchers then
compare these high-risk and low-risk groups on factors thought to mediate vulnerability. It has
been noted that when these proposed vulnerability factors are found in currently or formerly
depressed individuals, they might represent internal products (or ―scars‖) from past episodes
(Lewinsohn, Steinmetz, Larson, & Franklin, 1981). Such a factor might not be causally related to
depressive onsets. However, studies that find a proposed mechanism prior to a first onset
circumvent scar interpretations and support a vulnerability factor conceptualization.
Cognitive models of depression. It has long been proposed that negative cognition
initiates and maintains depression (Beck, 1967). From a cognitive diathesis-stress perspective,
vulnerable individuals are thought to possess a depressive cognitive schema that distinguishes
them from nonvulnerable individuals. This schema includes a ―negative triad‖ of core beliefs
Processing Biases 8
about the self, the world (e.g., indifference or disapproval from others), and the future (Beck,
1970). The depressive schema develops from recurrent, prolonged processing of negative
information. This elaborative processing activates, strengthens, and expands connected networks
of related depressive thoughts and representations of sad experiences. These myriad
interconnections sensitize the depressive schema and make it susceptible to self-sustained
patterns of spreading, reverberating activation (e.g., rumination; Bower, 1981; Ingram, 1984;
Nolen-Hoeksema, Morrow, & Fredrickson, 1993; Teasdale & Barnard, 1993). In essence,
depressed mood represents an affective-motivational state in which a mood-congruent shift in
processing causes depressive information to become more salient and accessible. Thus, cognitive
models predict that when activated, a depressive schema generates mood-congruent cognitive
products, such as negative self-attributions or automatic thoughts, or cognitive processes, such as
negative interpretive, attentional, or memory biases (Beck, Abramson, Seligman, & Teasdale,
1978; Williams, Watts, MacLeod, & Mathews, 1997). Further, these biases should be strongest
for information consistent with themes of sadness and loss, as opposed to fear or anger. This
latter content specificity hypothesis (Beck, 1976; Ingram, Miranda, & Segal, 1998) has
significant empirical support (see Beck & Perkins, 2001 for meta-analysis). When a depressive
schema is not activated, it is characterized as ―latent but reactive‖ to stress, such as a sad mood
induction (Segal & Shaw, 1986). This concept of cognitive reactivity dictates that depressotypic
cognitive processing and products should emerge when a depressive schema is activated, but not
necessarily when it is dormant.
Studies employing mood-priming largely support the idea of cognitive reactivity (see
Scher et al., 2005 for review). That is, formerly depressed individuals in a nondysphoric mood
usually cannot be distinguished from never depressed individuals in affective cognition.
Processing Biases 9
However, typically when these groups experience an affective challenge, such as a sad mood
induction, depressotypic cognitive patterns emerge only in the formerly depressed group. Studies
administering pharmacological challenges, such as acute tryptophan depletion (ATD), have also
evoked twin negativistic shifts in mood and information processing. These depressive effects of
altered central serotonergic function are pronounced in people who have remitted from
depression or who have elevated familial risk of depression (Merens, Booij, Haffmans, & van
der Does, 2008; Munafo, Hayward, & Harmer, 2006). Thus, within a sad mood, the cognition of
formerly depressed groups typically resembles that of currently depressed groups.
Negatively biased selective attention, interpretation, and memory have also distinguished
currently dysphoric groups from nondysphoric groups (e.g., Bradley, Mogg, & Lee, 1997; Gur et
al., 1992; Matt, Vazquez, & Campbell, 1992). Although cognitive models originally conceived
depressive biases to be strictly toward negative information, biases away from positive
information have also been found (Clark, Beck, & Stewart, 1990). Dual-valence biases appear
consistent with major depressive episode presentations, which are often characterized by both
increased negative affect and decreased positive affect (Clark & Watson, 1991). Similarly,
sometimes depressed individuals may lack a positive processing bias that psychologically
resilient individuals show. Under certain circumstances processing information in an accurate,
evenhanded fashion might stifle the motivational or mood-buffering benefits of an over-
optimistic cognitive bias (see DePue & Collins, 1999; Forgas & East, 2008a, 2008b).
As an important aside, recent studies employing pharmacological challenges or
psychophysiological techniques have reported negativistic cognitive processing in the absence of
sad mood in formerly depressed groups (Atchley, Ilardi, & Enloe, 2003; Atchley, Stringer,
Mathias, Ilardi, & Minatrea, 2007; Hayward, Goodwin, Cowen, & Harmer, 2005; Steidtmann,
Processing Biases 10
Ingram, & Siegle, 2010; Victor, Furey, Fromm, Öhmann, & Drevets, 2010). At least two
important theoretical and methodological issues arise from these findings. First, active cognitive
vulnerability factors may be present in nondysphoric depression vulnerable individuals. Second,
cognitive neuroscience techniques may complement traditional behavioral performance measures
of cognition to increase researchers’ ability to detect depressotypic processing patterns. For
example, if a study reveals neural activity differences but finds no behavioral performance
differences, there could be several possible explanations. Measures of neural activity might
provide greater sensitivity for detecting differences, neural activity differences might precede the
emergence of performance differences, or alternate areas of the brain might compensate to
maintain normal task performance (e.g., Drummond, Gillin, & Brown, 2001). Irrespective of
method or mood status, detected emotional biases in selective attention, inhibition, and memory
could serve as depression vulnerability or maintenance factors.
Selective attention involves discerning information that is relevant to a current objective
from irrelevant information, then activating relevant and inhibiting irrelevant information in
working memory (Houghton & Tipper, 1994; Neill, Valdes, & Terry, 1995). Accordingly,
Joormann (2004) proposed that depressotypic selective attention might partly result from an
inhibitory processing deficit for depressive information. Supportive evidence has been found in
depressed and depression susceptible groups (Goeleven, De Raedt, Baert, & Koster, 2006; Hsieh
& Ko, 2004; Joormann, 2004; Kuehner, Holzhauer, & Huffziger, 2007). Inhibition is also
thought to be instrumental in efficient memory encoding and retrieval. When an individual is
focusing on emotional information, a global selective attentional bias toward, or a specific
inhibitory deficit for, negative information might lead to increased elaborative processing of
negative content. With time and repetition, this pattern could strengthen connections among
Processing Biases 11
depressive cognitive structures (Ingram, 1984). Also, when an individual attempts to focus
attention on nonaffective task-relevant information but previously activated negative cognitive
content has been degraded insufficiently, the negative content could linger and contaminate
working memory. As a result, associations between task-relevant nonaffective information and
task-irrelevant depressive information could be encoded and later retrieved. Once this occurs,
depressive cognitive structures could be activated and strengthened by processing depressive or
associated nonaffective information (Linville, 1996).
Depressive cognitive residue could interfere with working memory, leading to difficulties
with the kinds of complex problem solving required in daily life (Siegle, Ingram, & Matt, 2002).
In turn, these difficulties might increase depressive thoughts and feelings. For this individual, the
omnipresent salience of a depressive schema could override attempts to focus on nonaffective
goal-relevant information processing, resulting in intrusive streams of depressive thoughts.
Therefore, selective attention biases for depressive facial affect might lead to negative,
ruminative thought patterns, which would perpetuate depressive moods (Nolen-Hoeksema et al.,
1993) and deeply encode depressive memories, the combination of which might initiate or
maintain depressive episodes. Prospective studies support that cognitive biases might contribute
to depressive onsets (e.g., Hammen & Goodman-Brown, 1990; Robinson & Alloy, 2003).
Interpersonal models of depression. Interpersonal models of depression hypothesize that
specific social behaviors and social deficits maintain or exacerbate depressive episodes by
impeding rewarding social experiences and increasing punishing ones. For instance, a depressed
woman may excessively seek reassurance from others, eventually leading to the erosion of close,
supportive relationships. Initially, well-meaning relational partners may validate that she is
valued and loved, reinforcing reassurance-seeking behavior. However, as requests for
Processing Biases 12
reassurance increase and persist, relational partners may become irritated and emotionally distant
(Joiner & Coyne, 1999). Alternatively, negative self-verification behaviors might also promote
social conflict and rejection (Swann, Wenzlaff, Krull, & Pelham, 1992). A depressed man may
insist that significant others evaluate him negatively, verifying an unfavorable self-concept.
Relational partners who confirm his negative self-concept further perpetuate his depression.
Relational partners who dispute his negative self-evaluation without any persuasive effect may
become frustrated and may avoid or reject him. Evidence supports the existence of depressive
interactional patterns proposed by these models (Joiner, Metalsky, Katz, & Beach, 1999; Swann,
Wenzlaff, & Tafarodi, 1992).
Depression has also been associated with social skills deficits, including poor conflict
resolution skills, insufficient eye contact, and deficient ability to foster rewarding, supportive
relationships (Lewinsohn, 1974). Although depressed individuals may be more interpersonally
dependent (Barnett & Gotlib, 1988), their interaction patterns tend to be aversive to others. As a
result, others may avoid them or react negatively toward them (Gilboa-Schechtman, Erhard-
Weiss, & Jeczemien, 2002). As more superficial friendships dissolve, a depressed individual may
more desperately lean on remaining close relationships. Greater relational strain may instigate
corrosive social conflicts that a depressed person struggles to resolve (see Whisman, 2001). It is
common for relational conflicts to trigger or exacerbate depression (e.g., Monroe, Rohde, Seeley,
& Lewinsohn, 1999; Paykel et al., 1969). Therefore, interpersonal models effectively explain
how a depressed person can become mired in a social environment that is increasingly punishing
and unrewarding (Joiner & Coyne, 1999) without the social skills or resources to obtain social
reinforcement or support (Gotlib & Hammen, 1992).
Given the nature of these social deficits, interpersonal models and facial affect processing
Processing Biases 13
might interrelate in several ways. Depressed groups might inaccurately identify others’ affects,
and this impairment might be linked to social dysfunction, excessive reassurance seeking, or
rejection. They also might attend less to others’ facial affect, find others’ affect more aversive,
and be less willing to engage with people displaying any affect, even happiness.
Cognitive-interpersonal model of depression. Gotlib and Hammen (1992) have
identified developmental interrelationships between cognitive and interpersonal factors linked to
depression susceptibility. Their synthesis describes how interpersonal behaviors and experiences
can influence self-esteem, perceived self-worth, and expected responsiveness of others. In turn,
this mindset may promote social behaviors and experiences that initiate or maintain periods of
depression. In particular, Gotlib and Hammen draw attention to conceptual similarity between
attachment and cognitive theories. According to Bowlby (1969, 1981), during the normal parent-
child attachment process, a child develops a mental representation, or working model, of close
relationships. This representation includes assumptions about what can be expected from others
and a relative sense of one’s importance to others. However, patterns of neglect, abuse, and harsh
judgment would thwart the development of secure attachment and self-esteem. This
conceptualization melds well with Beck’s negative cognitive triad. Combined, attachment and
cognitive approaches describe how a person could develop low self-esteem and expectations that
the social world will forever be unwelcoming and unmanageable.
Empirical findings support the plausibility of these interacting processes. For example,
those who develop depression tend to experience worse care in early family environments (e.g.,
Hammen, 1991). Poor parenting has been shown to predict lower social competence in children,
which, along with insecure attachment, predicts depressive symptoms longitudinally (Gotlib &
Hammen, 1992; Hammen, 1991; Lee & Hankin, 2009). Particularly important to Gotlib and
Processing Biases 14
Hammen’s synthesis, the influence of harsh parenting during childhood on adult depression may
be mediated by negative cognition (for review see Alloy, Abramson, Smith, Gibb, & Neeren,
2006). The cognitive-interpersonal model might be further enriched by findings that connect
abnormal facial affect processing with core constructs, such as early adverse interpersonal
experiences, social dysfunction, and negative cognitions about the self and the social world.
Social risk hypothesis. Evolutionary theorists have speculated that depressive social
behavior and cognition emerges from a normally adaptive depressive mechanism (Allen &
Badcock, 2003; Gilbert, 2001; Nesse, 1998; Nettle, 2004) that becomes maladaptive when
chronic activation leads to health-compromising major depression. Allen and Badcock’s (2003)
postulated mechanism manifests at multiple levels (social behavior, information processing,
neural activity) to help maintain tenuous social inclusion following significant reduction in social
capital. Specifically, their social risk hypothesis proposes that transient depressed states evolved
as a cautious mode of interaction that emerges when one’s perceived social burden on others
meets or exceeds his or her perceived social value to others. If this value-to-burden ratio—
referred to as social investment potential–drops below 1.0, a person becomes prone to exclusion
from social bonds that historically have facilitated survival and reproduction. Depressive
symptoms often follow a social loss, humiliation, or failure—decreases in social value. A man
with suddenly less to offer an interdependent social system could re-balance his social
investment potential and maintain social inclusion by conspicuously reducing his consumption of
limited group resources (Allen & Badcock, 2003). His submissive behavior would decrease
group competition for mates, food, and property. From this perspective, temporary decreased
appetite, anhedonia, social isolation, and feelings of worthlessness and hopelessness are products
of an adaptive depressive mechanism.
Processing Biases 15
Allen and Badcock’s proposed depressive-mood-activated mechanism engages patterns of
thought, emotion, and behavior that decrease social burden and protect damaged social value.
This social risk-averse strategy is marked by reluctance toward social interactions, except those
with predictable, safe outcomes. To this end, the strategy promotes vigilance to and avoidance of
any interaction with the slightest chance of social devaluation. As such, a depressed individual
would be unlikely to seek out interactions with unfamiliar people. Even an apparently friendly
stranger could turn out to be an outcast or a deceitful manipulator. Instead a depressed person
would try to bolster existing close relationships (Gilbert, 1992). From this perspective, both
reassurance seeking and negative self-verification behaviors may help discern dependable
relationships amid perceptions of low self-worth.
Most relevant to this review, Allen and Badcock’s (2003) depressive mechanism
presumably promotes greater cognitive sensitivity and attention toward socially threatening
information and away from socially affirming information. The mechanism also negatively
biases interpretation of social information, such that ―negative conclusions are drawn from any
semblance of social threat, no matter how minor‖ (Allen & Badcock, 2003, p. 899). Although
Allen and Badcock focus on hypersensitive social-perceptual processes, it follows that memory
for past social threats would aid a risk-averse strategy. Facial affect would be a particularly
important reference to gauge one’s social value and to avoid potentially risky social interactions.
Allen and Badcock do not detail which facial affects might connote risk. However, based on
their theory, face-to-face expressions of neutral, angry, contemptuous, sad, fearful, disgusted,
and mixed affect might foreshadow unpredictable and socially hazardous interactions (Adams,
Ambady, Macrae, & Kleck, 2006; Fischer & Roseman, 2007; Hendriks & Vingerhoets, 2006;
Knutson, 1996; Rozin, Haidt, & McCauley, 2008). From a depressive framework, a frown could
Processing Biases 16
indicate displeasure, a fearful or disgusted expression could indicate aversion to the person’s
presence, and angry affect could signify irritation or even physical threat. Thus, ensuing
interactions would carry the potential for social devaluation. Until now, the accuracy of social
risk hypothesis predictions has not been evaluated based on the depressive facial affect
processing literature.
The importance of facial affect processing.
Human evolution. We have come to understand that facial affect perception is important
in human social development, social function, and evolution. Charles Darwin (1872) observed
the similarity of specific human emotional expressions (e.g., laughter, fear, rage, dejection)
across different ages, races, and cultures, and also in comparison with non-human primates. A
century later, Paul Ekman (1992) presented evidence of universal human facial expressions of
basic emotions (e.g., happiness, sadness, anger, disgust, fear, surprise). He also taxonomized
expression-specific facial musculature contraction and relaxation patterns (Ekman & Friesen
1978), instructions for which are presumably encoded in our genome. It appears facial
expressions of emotion have been preserved through evolution because they serve an adaptive
purpose.
Emotional facial expressions function as a fundamental paralanguage for communicating
basic needs, a medium that preceded the development of language in human evolution (Wilson,
1999). Perceiving facial identity and affect allows a person to recognize family, friends,
acquaintances, and rivals; to assess their internal mental and mood states; and to obtain
immediate feedback within every interaction (Cosmides & Tooby, 2000). This information can
be used to predict a person’s intentions and ensuing behaviors. For example, facial affect can
signal social danger to be averted. Accordingly, humans judge angry, fearful and disgusted faces
Processing Biases 17
as more interpersonally critical and harsh (Stein, Goldin, Sareen, Zorrilla, & Brown, 2002).
Humans also more quickly attend to and more accurately identify angry faces (Krysko &
Rutherford, 2009). In turn, other people’s facial affect can effectively guide one’s pursuit of
relational goals (e.g., reassure trust, establish dominance or submissiveness, or disengage) and
define relational status (Leathers, 1997). For instance, humans are less willing to approach and
interact with a person displaying angry or disgusted facial affect (Campbell et al., 2009), as this
person might threaten one’s social position or safety. In terms of interpersonal outcomes, facial
affect decoding ability correlates with social competence, social adjustment, and peer popularity
(Leppӓnen & Hietanen, 2001). Throughout our ancestral history, facial affect processing would
have been indispensable in solving problems related to survival, co-existence, and reproduction.
The ability to decode facial affect and react accordingly has been critical for satisfying
interaction partners, fostering close attachments, and building alliances (Elfenbein & Ambady,
2002; Marsh, Kozak, & Ambady, 2007; Montagne, Kessels, Frigerio, de Haan, & Perrett, 2005).
For countless millennia these bonds have facilitated reliable access to communally gathered
resources, mating opportunities, and protection for oneself and one’s offspring. In contrast,
abandonment and ostracism have threatened extinction for the individual and his or her genes. In
this way, genes that program mechanisms to execute facial affect perception and the social skills
that depend on this processing would have provided a selective advantage (Dawkins, 1976).
Social development and functioning. Humans are programmed to transmit and receive
emotional information via face-to-face visual interaction. Days after birth, infants start to
discriminate happy from sad faces, show basic facial imitation (Field, Woodson, Greenberg, &
Cohen, 1982), and show a preference for their mother’s face (Johnson & Morton, 1991). This
early attentional orienting to a mother’s face encourages bonding interactions, which stimulate
Processing Biases 18
the release of dopamine, prolactin, oxytocin, and endorphins that produce shared enjoyment,
contentment, and emotional attachment. Recurrent interactions like this help develop neural
networks of attachment, which include the orbitofrontal cortex (Panksepp, 1998; Schore, 1994).
The orbitofrontal cortex is particularly important in assessing nuanced reward and punishment
contingencies in dynamic social interchanges. As such, orbitofrontal damage impairs recognition
and use of facial affect to assess reinforcement value or gauge approachability in interpersonal
situations (Hornak et al., 2003; Zald & Kim, 2001). Thus, throughout life, an optimally
functioning orbitofrontal cortex is critical for obtaining desired social outcomes. Also, face-to-
face infant-mother interactions bring online networks responsible for contracting the infant’s
facial muscles to imitate its mother’s expression. This initially reflexive mirroring process carries
on into adult life at a subtle but measurable level (Dimberg, Thunberg, & Elmehed, 2000).
Facial mirroring has several important consequences. Typically, a primary caregiver
perceives emotional responsiveness from the infant and reciprocates with increased interest,
attention, and positive emotion. For the infant, such attunement could also help build working
models of attachment figures as being responsive and caring. Similarly, face-to-face interactions
may help establish mental representations of the world as being safe and manageable, or
dangerous and overwhelming (Schore, 1994). These important mental representations would be
well-developed before linguistic and narrative interpretation capabilities are fully functional
(Chapman, 2000; Gazzaniga, 1989). In other words, a model of the socio-emotional landscape is
established before the words to describe it. This primary source material for approach and
avoidance decisions is activated in the brain’s right-hemisphere and gets expressed as
unconscious intuitions (Cozolino, 2002). Lastly, facial mirroring may help develop bidirectional
connections between emotions and facial expressions. Through bidirectional connections,
Processing Biases 19
positive emotion can prompt a genuine Duchenne smile, or intentionally contracting muscles that
are typically used to smile can induce positive emotion (Soussignan, 2002). Capitalizing on a
baby’s developing emotion-facial expression connections, facial mirroring with a caregiver may
also help establish rudimentary emotion regulation.
A newborn possesses limited ability to regulate emotion. When the baby becomes upset, a
parent may initially attend to and mirror the child’s distress. The parent may then soothe the
baby through touch, verbal inflection, and calm facial expression. In turn, the baby’s facial
mirroring system may slowly attune to the parent’s calm expression. Through facial muscle-
affect connections, the child’s underlying emotions also become more in line with the parent’s.
Simultaneously, these interactions scaffold the development of the baby’s inhibitory neural
networks. Over time, the parent’s emotion regulation becomes internalized in networks that
enable the child to self-soothe (Cozolino, 2002). Furthermore, these experiences teach the child
that attachment relationships can help regulate emotion and facilitate contentment.
When a parent has depression, attunement can backfire for a child. Rather than being
soothed by a parent’s interest and positive affect, a child might mirror his depressed parent’s sad
facial affect. Evidence indicates that infants show increased right frontal EEG asymmetry
consistent with negative affect when viewing sad versus happy faces (Diego et al., 2004). In
effect, a depressed parent’s affective experience would become the child’s (Gunnar & Stone,
1984). Potentially as a basic mood regulation strategy, an infant will tend to look less at his
mother’s face if she is depressed (Boyd, Zayas, & McKee, 2006). Without a stable parental
scaffold to construct robust emotion regulation, a child’s inhibitory capacity might not fully
develop. This would result in deficient self-soothing. Furthermore, Cozolino (2002) proposes
that in the midst of this type of suboptimal attachment experience, a child acquires a
Processing Biases 20
hypersensitivity to negative social cues, such as facial expressions (see Joormann, Talbot, &
Gotlib, 2007 in this review). Interestingly, research on serotonin transporter gene variants (5-
HTTLPR) linked to increased depression risk indicate that environmental factors during
development may help calibrate a pattern of increased neural responsiveness to negative facial
affect (e.g., Gibb, Benas, Grassia, & McGeary, 2010; Wolfensberger, Veltman, Hoogendijk,
Boomsma, & de Geus, 2008). Notably, it is challenging to separate environmental and genetic
influences in studies examining signs of emotional vulnerability in children of depressed parents.
However, it is plausible that early facial affective attunement processes help shape the
development of emotional attachment and emotion regulation.
Functional neuroanatomy of facial affect processing. Characteristics of the human
neural system supporting facial affect perception further corroborate the evolutionary and social
significance of emotional expressions. Substantial evidence indicates that the amygdala, the
orbitofrontal cortex, and fusiform gyrus of the right temporal cortex are instrumental in
processing facial affect (see Rolls, 2008 for review). In fact, specific neurons in the amygdala,
orbitofrontal cortex, and superior temporal sulcus activate preferentially to affective faces.
Dysfunction in these regions can result in impaired social and emotional behavior. More
complex theoretical models have described specialized subsystems involving additional brain
regions, such as the parietotemporal cortices, insula, thalamus, dorsal anterior cingulate, and
cerebellum (Gerber et al., 2008; Haxby, Hoffman, & Gobbini, 2000; also see Fusar-Poli et al.,
2009 fMRI meta-analysis). Although cortical activity promotes conscious perception of
emotional expressions, subcortical processing can efficiently and independently decode facial
affect. One effect is that humans can read and react to facial affect that they have not consciously
Processing Biases 21
seen (Vuilleumier & Schwartz, 2001; Whalen et al., 1998). Thus, the brain appears to have
evolved rapid, specialized facial affect processing (Schore, 1994).
Heller and colleagues (1993, 1998) proposed a model that might help explain depressive
responses to facial affect. The model suggests that experience of emotion is mediated by a frontal
valence system, and the perception of emotion is lateralized in a right parietotemporal arousal
system. Further, these systems are functionally interconnected (see Leathers, 1997 for similar
conceptual discussion of categorical and dimensional aspects of face processing). In support of
the valence-arousal model, resting state electroencephalographic (EEG) studies show that
depression prone groups exhibit less left (relative to right) frontal activity (Jones, Field, Fox,
Lundy, & Davalos, 1997; Tomarken, Dichter, Garber, & Simien, 2004), a pattern linked to
negative emotion and reduced social approach behaviors (Davidson, 1993). They also show
abnormally reduced right-than-left posterior activity during resting state (Bruder, Tenke, Warner,
& Weissman, 2007) and in response to affective faces (Deldin, Keller, Gergen, & Miller, 2000;
Jaeger, Borod, & Peselow, 1987), a pattern related to impaired facial affect identification (Heller
et al., 1998). Impaired facial affect discrimination due to right posterior hypoactivation might
maintain depressive experiences and behaviors, as represented by left frontal hypoactivation. For
instance, a depressed man might not register another person’s subtle smile and would thus not
experience its positive emotion.
Major depression is also often characterized by amygdala dysregulation (Mayberg, 1997).
In particular, depression has been strongly linked to greater amygdala reactivity to sad faces and
lower reactivity to happy faces (Dannlowski et al., 2007b; Surguladze et al., 2005; Suslow et al.,
2010). This pattern may be reversible with antidepressant medication (Victor et al., 2010). Given
that cognitive biases can disappear when depression remits, it is not surprising that effective
Processing Biases 22
treatment can lead to changed neural responses to facial affect. Interestingly however, Fu and
colleagues (2004, 2008) found that mode of treatment differentially affected specific neural
activity changes. Cognitive behavioral therapy affected mainly cortical functioning, while
antidepressants led to cortical and subcortical changes. In summary, facial affect processing
requires the coordinated activity of many regions of the brain (Vuilleumier & Pourtois, 2007),
and abnormal regional function can be linked to depressotypic processing biases.
Information Processing Tasks Employed in the Literature
Many studies have compared healthy and depression-prone groups’ attention to affective
facial stimuli. Studies assessing for attentional biases have used dot-probe, face-in-the-crowd,
negative priming, exogenous cueing, and deployment of attention tasks (DOAT). Other studies
have employed affect identification tasks or memory tasks. In these studies, behavioral measures
such as accuracy and reaction time are the key dependent variables.
Deployment of attention task. On each deployment of attention task trial, two side-by-
side affective faces (e.g., negative and neutral, positive and neutral, or positive and negative) are
presented simultaneously. However, participants are misinformed that one face will appear
slightly before the other face, and they are instructed to identify which face appears first. It is
inferred that the face the participant chooses in each trial is the first to become consciously
available because it has received greater attentional allocation.
Dot-probe task. To begin each dot-probe task trial, two affective facial expressions (e.g.,
positive-neutral, negative-neutral, or positive-negative) of the same actor appear on opposite
locations of a computer screen. The pair of faces is quickly replaced by a single dot, which
appears in place of one of the faces. The participant is to identify the position of the dot by
pressing one of two buttons as quickly as possible. If attentional allocation is greater toward the
Processing Biases 23
face where the dot subsequently appears, the dot’s position will be identified faster. In contrast, if
attention is greater toward the face opposite the location where the dot subsequently appears, the
response time will be delayed.
Exogenous cueing task. The exogenous cueing task (Posner, 1980) attempts to measure
selective attention toward useful information and attention inhibition of potentially distracting
information. At the start of each trial, a cue stimulus (e.g., a face) is briefly presented in one of
two opposite locations on a screen, and then a target square is presented in either the same or the
opposite screen location. Participants must watch for the target square and, when it appears, they
quickly press a button corresponding with the square’s location. In any given trial, the cue either
serves as an accurate predictor, pulling attention toward the spatial location of the subsequently
presented target square, or it serves as a distracter, pulling attention away from the location of the
subsequently presented target square. A faster response to the target square when the cue is an
accurate predictor is known as the cue validity effect. This effect is expected to increase with the
amount of attention a cue captures (Leyman et al., 2007).
Face-in-the-crowd task. The face-in-the-crowd task assesses how efficiently one detects
specific facial emotions within slides that include between two-to-six schematic faces. Schematic
faces are rudimentary line drawings conveying happy, sad, or neutral affect. Schematic faces
stand in for human faces in this task because facial emotion detection presumably occurs rapidly
and efficiently based on low-level perceptual features (Purcell & Stewart, 1988). Some slides
show faces with identical affect, while other slides include one emotional expression that
contrasts with surrounding expressions (e.g., one happy among three neutrals). In response to
slides, participants are to rapidly decide if all expressions are identical or if one is different.
Facial affect identification tasks. Ineffective facial affect identification can result from
Processing Biases 24
insensitive detection of various affective intensities (e.g., subtle happiness) or from
misinterpreting the specific categorical meaning of facial affect (e.g., fear rather than anger).
Thus, investigators have varied methodological elements to examine affect identification
sensitivity and specificity in depression prone groups. In these tasks, participants view facial
stimuli and then either label the displayed emotion or attempt to match the stimulus to a model
face with the same emotion. Depending on the study, participants view stimuli anywhere from
briefly (e.g., 100 ms) to as long as they need to respond. These stimuli either show prototypical
facial affect (i.e., 100% sad) or ambiguous morphed gradations of two affects (e.g., 50% happy
and 50% neutral). Some studies have had participants discriminate only among happy, sad, and
neutral affects, while others have had participants identify as many as seven different affects.
Negative priming task. A negative priming paradigm that utilizes affective facial stimuli
can index cognitive inhibition of specific emotional content. On each trial, two pairs of affective
faces are presented in succession (prime then probe). For each presented pair the participant must
quickly press a button corresponding to an identified target face (e.g., framed in black) and
ignore a distracter face (e.g., framed in gray). When a specific affect appears first as a prime
distracter and then as a probe target, the probe target response is typically delayed. This is known
as the negative affective priming effect. On such a trial, a person might need to inhibit processing
of a first sad face and soon thereafter respond to a second sad face. To the degree that inhibition
of the prime distracter affect (e.g. sadness) carries over into the probe trial, it will slow responses
to probe targets of the same affect (e.g., sad faces). When this negative affective priming effect is
missing it typically indicates an attentional inhibition deficit.
Oddball task. A facial oddball task requires participants to ignore viewed faces from one
predominant affective category and to respond to faces from a different, rarely presented target
Processing Biases 25
category. For a particular block, participants might view 80% neutral faces and respond only to
the 20% of presented faces, which convey happy affect. As such, the task elicits selective
attention for a particular infrequently presented target affect. The task also can evoke response
inhibition if rare non-target affective distracter stimuli are presented.
Recognition memory task. To begin a recognition memory task, participants encode
affective facial expressions either intentionally or unintentionally by completing another
information processing task that includes those stimuli (e.g., affect identification task). After a
delay, participants are challenged to identify only previously viewed (―old‖) faces in a set that
also includes novel (―new‖) faces. Typically, the familiar faces in the recognition phase are the
identical images (i.e., same actor, same affect) presented in the encoding phase.
Remember/know/guess task. In this recognition memory task, participants first
intentionally encode images of affective facial expressions displayed by actors. Later, they view
neutral expressions of old and new actors, and are instructed to identify only those actors who
were presented in the encoding phase. Participants answer ―remember‖ if they both recognize the
identity of the actor and the specific affect the actor had displayed at encoding. They respond
―know‖ when they recall the actor but not the actor’s previously displayed affect. They respond
―guess‖ if they are unfamiliar with the actor or the previous affect.
Cognitive Neuroscience Measures Employed in the Literature
A collection of studies has employed cognitive neuroscience to examine brain correlates
of facial affect processing in depressed and depression vulnerable groups.
Event-related potentials techniques. Orienting, engagement, disengagement, and
shifting of attention are events that take place on a very brief time scale. For this reason, event-
related potentials (ERP) research, with its precise temporal resolution, has been most effective in
Processing Biases 26
elucidating how attentional processes occur in time within particular neurocognitive systems.
Several ERP waveform components correspond with distinct stages of attention, including low-
level stimulus feature processing, high-level abstract classification, and sustained engagement.
The present review focuses on the N200 and P300 because these components have been linked to
depressive attentional and classification biases. ERP components appear as positive or negative
amplitude peaks or troughs within specific time windows of the waveform. For example, a P300
is the third positive deflection that appears roughly 300-500 milliseconds (ms) post-stimulus.
The P300 may represent the amount of attentional resource allocation, which is inferred by the
component’s observed amplitude (Debener, Kranczioch, Herrmann, & Engel, 2002). The N200
is the second negative waveform deflection that peaks around 250 ms. An N200 appearing in
frontal scalp locations may represent conflict monitoring and/or inhibition, as in detecting and
suppressing information processing or behavioral responses that conflict with a primary task goal
(Jonkman, 2006). In contrast, an N200 appearing in a right posterior scalp location can reflect
neural activity related to facial processing (e.g., Deldin et al., 2000).
Functional neuroimaging. Neuroimaging techniques such as functional magnetic
resonance imaging (fMRI) and positron emission tomography (PET) have been used to monitor
physiological correlates of brain activity in response to passively viewed affective stimuli. fMRI
detects regional recruitment of oxygenated blood, while PET detects changes in regional glucose
metabolism. With both techniques, physiological responses trail cognition by a couple seconds.
However, these techniques can precisely localize and measure sustained cognitive processing
within networked neural structures.
Summary
As discussed, facial affect processing is essential to social development and functioning,
Processing Biases 27
and is particularly relevant to models of depression. We therefore review the burgeoning
depressive facial affect processing literature and examine its potential for integrating disciplines,
theories, and research. Two main questions guide this review. First, what do information
processing and cognitive neuroscience studies indicate about facial affect processing in
depression and depression susceptibility? Second, what support can such studies provide for
cognitive, interpersonal, cognitive-interpersonal, and social risk models of depression? Based on
the extant literature, we identify ways in which future research might yield greater understanding
of facial affect processing and depression susceptibility.
Method
This review was influenced by AMSTAR recommended criteria for systematic reviews
(Shea et al., 2007). It focuses on empirical studies that examine information processing of
affective facial stimuli in depression susceptible samples.
Literature Search
To capture relevant studies, we completed thorough searches on several online databases,
including PsycINFO, MEDLINE, PUBMED, and Psychiatry Online. We used numerous search
terms: words beginning with the root depress, face, facial, face processing, expression, affect,
emotion, bias, information processing, interpretation, recognition, attention, memory, recall,
imaging, psychophysiology, and ERP. We also examined reference sections of studies meeting
inclusion criteria for any additional relevant studies.
Inclusion and Exclusion Criteria for Included Studies
We included in the present review studies that met the following criteria. We selected
articles that were written in English and were identified by searches through November 2010.
We included articles irrespective of publication status to reduce potential publication bias. To
Processing Biases 28
refine our search, we selected only studies that included a significant, identifiable proportion of
participants with current major depressive disorder or risk for depression. To this end, included
studies assessed the presence of past or present depression as defined by the Diagnostic and
Statistical Manual, Fourth Edition, Text Revision (American Psychiatric Association, 2000). We
considered study subsamples to be at-risk for depression if an assessment indicated a personal or
parental history of depression or a measurement of stable, elevated depressive symptoms. In
addition, we reviewed only studies that included a low-risk or nondepressed control group to
enable between-group contrasts. Only these studies can assess for specific depressive processing
biases. Related to this, we described only studies that assessed facial affect identification,
attention, or memory with information processing tasks. As such, we acknowledged, but did not
thoroughly review, findings derived from passive viewing tasks. This applied to certain
neuroimaging studies that more broadly implicate structures involved in facial affect processing.
Principally, we included research with adult samples, with the exception of studies of children
and adolescents known to possess greater risk for developing adult depression. In addition, we
summarize only studies with samples indicating experience with or risk for unipolar depression,
excluding studies in which a significant portion of the depressed sample met bipolar criteria.
Forty-seven studies met inclusion criteria and are reviewed below.
Results
Identification of Facial Affect
One’s ability to identify facial affects may be normal, generally impaired, or specifically
biased/impaired. General impairment implies decreased accuracy in identifying an array of
different facial affects and may be a facet of more pervasive cognitive deficits. As such, general
impairment might decrease one’s understanding and interaction skill in a wide variety of social
Processing Biases 29
situations. In contrast, specific impairment refers to insufficiently recognizing select affects;
identification of most affects is normal. Specific bias can refer to abnormally increased
identification of select affects. Also, consistently misinterpreting neutral affect as negative and
positive affect as neutral would constitute a negative cognitive bias. Specific bias or impairment
might impact one’s social understanding and behavior in certain situations but not others.
Normal identification. There is substantial evidence suggesting that facial affect
identification in depression susceptible groups can be normal (affect identification studies are
summarized in Table 1). Many depressed and dysphoric samples have shown unimpaired,
unbiased identification of unambiguous, prototypical positive and negative facial affects (e.g.,
Archer, Hay, & Young, 1992; Beevers, Wells, Ellis, & Fischer, 2009; Deldin, Keller, Gergen, &
Miller, 2001; Frewen & Dozois, 2005; Gaebel & Wolwer, 1992; Gollan, Pane, McCloskey, &
Coccaro, 2008; Gollan, McCloskey, Hoxha, & Coccaro, 2010; Leppӓnen, Milders, Bell, Terriere,
& Hietanen, 2004; Milders, Bell, Platt, Serrano, & Runcie, 2010). These results have usually
emerged with longer stimulus presentations, which allow more time for considering responses.
General impairment. Several other studies have reported general affect identification
impairments in depressed groups (Feinberg, Rifkin, Schaffer, & Walker, 1986; Persad & Polivy,
1993; Surguladze et al., 2004; Weniger, Lange, Ruther, & Irle, 2004). Studies reporting general
deficits have been somewhat methodologically distinct from studies reporting no between group
differences or specific depressive biases/impairments. First, studies reporting a general deficit
have more consistently included depressed inpatients. Depressed inpatients are more likely than
depressed outpatients or dysphoric community members to exhibit pervasive cognitive deficits,
such as slowed processing and impaired concentration and memory, which also correlate with
facial affect perception deficits (Csukly et al., 2011). This is consistent with findings that more
Processing Biases 30
acute levels of distress are associated with decreased ability to discriminate between emotions
(van Marle, Hermans, Qin, & Fernández, 2009). Possibly related to this, more genetically-
mediated depression, which tends to be more pernicious (McGuffin, Katz, Watkins, &
Rutherford, 1996), appears linked to attenuated amygdala responses to facial affect
(Wolfensberger et al., 2008). Increased baseline amygdala activity coupled with phasic
unresponsiveness to facial expressions could undermine affect discrimination. Second, some
studies reporting general deficits have presented participants with significantly fewer trials-per-
affect (e.g., 1-3 trials x 7-8 affects; Feinberg et al., 1986; Persad & Polivy, 1993; Weniger et al.,
2004) than studies reporting valence-specific differences have presented (e.g., 15-50 trials x 3-7
affects). Fewer trials would lead to comparatively lower power to detect any simple main effects
emerging from a group-by-affect interaction. As such, Feinberg et al. (1986) only assessed for
general impairment in identifying affects. Although Persad and Polivy (1993) did test for
valence-specific effects, visual analysis of their data suggests that specific difference trends
might have reached significance with greater power. Third, some studies reporting general
deficits have used briefer stimulus presentations (e.g., 100 ms, 500 ms; Feinberg et al., 1986;
Surguladze et al., 2004). In the case of depressive cognitive slowing, briefer presentations might
impede sufficient elaborative processing, impairing affect discrimination (Cooley & Nowicki,
1989). Complementary with this explanation, specific depressive cognitive biases typically
emerge only when longer stimulus presentations enable post-automatic elaborative processing
(Bradley et al., 1997). Thus, shorter presentations, fewer trials, and increasing depressive
severity may help explain why general facial affect processing deficits have been found in some
depressed groups.
Processing Biases 31
Specific impairments/biases. Still other studies have found impairments or biases in
identifying specific affects in depressed and dysphoric groups. Wright et al. (2009) found that
depressed women were less accurate than nondepressed women in discriminating brief
presentations of sad and fearful facial affects, but were not different in discriminating happy and
angry affects. Similarly, Ridout, Noreen, and Johal (2009) found that a group with naturally
occurring dysphoria showed impaired categorical identification of sad and neutral faces. This
second result was conceptually consistent with two previous findings. First, depression has been
associated with less accurate identification of neutral faces (Leppӓnen et al., 2004). Second,
although moderate depression can be linked to more sensitive identification of sadness (Gollan,
et al., 2010; Milders et al., 2010), acute levels of negative affect have been associated with less
accurate discrimination of sadness (Gur et al., 1992). This latter finding could be due to
stimulus-evoked hypoactivation of brain regions responsible for attentional control, evaluation of
social reward and punishment contingencies, and emotion regulation (e.g., caudate,
hippocampus, and orbitofrontal and dorsolateral prefrontal cortices, as identified by fMRI; Lee et
al., 2008). Seemingly in contrast, Hale (1998) reported that depressed outpatients rated higher
levels of sadness in several nonambiguous-affective categories of schematic faces. However,
these four findings may be complementary and not contradictory. Evidence suggests that
increased depression severity may be related to decreased specificity in differentiating between
sad and neutral affects and a generalized over-sensitivity to perceive sadness across the spectrum
of negative affective expressions (van Marle et al., 2009).
Indeed, there is relatively clear evidence that depression, naturally occurring dysphoria,
and pharmacologically induced dysphoria are associated with abnormal interpretation of subtle
or ambiguous facial affect (e.g., Gollan et al., 2010). In comparison to healthy control groups,
Processing Biases 32
depressed and dysphoric groups have exhibited lower sensitivity in identifying happy-neutral
facial affect (Gur et al., 1992; Joormann & Gotlib, 2006; Yoon, Joormann, & Gotlib, 2009).
Similarly, LeMoult and colleagues (2009) found that a formerly depressed group primed with sad
mood was less sensitive to lower intensities of happy facial affect than a never depressed control
group. Conceptually consistent with these results, depression has been associated with an
attenuated right posterior N200 ERP component during the identification of positive facial affect
but not positive words (Deldin et al., 2000). The location of this attenuated N200 for happy faces
suggests reduced processing in the right parietotemporal cortex and fusiform face area,
consistent with Heller’s regional specialization model of depressive cognition. These regions are
more specialized for visuospatial processing and might be less likely to show abnormally
decreased response to positive words, because words require only basic right-posterior visual
processing (Cohen et al., 2000). Subcortically, depression has been linked to attenuated
amygdala reactivity to subliminally presented positive facial affect (Suslow et al., 2010).
Supported neurobiological reward-motivation models indicate that insensitivity to reward
incentive stimuli, such as smiling faces, would stifle motivational states that drive social
approach behaviors and reinforcement attainment (Depue & Collins, 1999).
Depression has also been associated with increased sensitivity to neutral-sad and neutral-
fearful subtle affects (Beevers et al., 2009). Possibly related to this, Merens and colleagues
(2008) found that a remitted formerly depressed group identified neutral-disgusted faces faster
following acute tryptophan depletion. This procedure temporarily reduces available serotonin
and can induce a transient relapse of depressive symptoms (also see Surguladze et al., 2010
regarding increased psychophysiological reactivity to disgust faces in depressed group).
Additionally, ATD was linked to impaired specificity in identifying ambiguous fearful affect in
Processing Biases 33
this group. However, the sample showed no post-ATD changes in processing ambiguous happy,
sad, or angry faces. While these null results cannot be meaningfully interpreted, the authors
posited a mood-congruency effect for disgust. Specifically, they suggested that participants’
more efficiently processed disgusted faces due to the recent unpleasant gustatory experience of
consuming a tryptophan depleting drink.
Other studies have evoked biased responding through forced-choice paradigms in which
participants view neutral or ambiguous faces and must provide an affect label or intensity rating.
For example, Gollan and colleagues (2008) found that a depressed group interpreted neutral
faces as sad significantly more than a nondepressed group. Likewise, Hale (1998) reported that a
depressed group perceived more sadness in ambiguous schematic faces than controls.
Interestingly, Raes et al. (2006) found that the perceived intensity of negative facial affect across
a set of schematic faces significantly correlated with endorsed rumination. The neural
instantiation of negative interpretive bias is somewhat speculative. An intriguing possibility is
that this bias could occur due to the right amygdala remaining activated from previously viewed
negative facial affect. Two studies have shown that increased amygdala reactivity to
unconsciously viewed negative faces predicts negatively biased interpretations of subsequent
consciously viewed neutral faces (Dannlowski et al., 2007a, b).
Correlates of affect identification performance. Facial affect identification performance
appears to be related to the course of depressive episodes as well as shorter-term mood changes.
For example, perceiving greater levels of negative affects, such as disgust, fear, rejection, and
particularly sadness in ambiguous facial expressions has been found to predict a more prolonged
course (Geerts & Bouhuys, 1998; Hale, 1998; but see also Bouhuys, Geerts, Mersch, & Jenner,
1996). Further, continuing to perceive greater levels of negative facial affect into remission has
Processing Biases 34
been found to predict greater risk of relapse (Bouhuys, Geerts, & Gordijn, 1999). Additionally,
investigators have described how negative biases in facial affect interpretation can be
manipulated pharmacologically and how remission of such biases may serve as reliable
indicators of treatment outcome (Merens, Willem Van der Does, & Spinhoven, 2007; Venn,
Watson, Gallagher, & Young, 2006). However, other research has suggested that mood
congruent facial affect interpretation shifts can be induced in healthy individuals by a negative
mood prime. Some of this research has produced results similar to those found with depressed
groups (Bouhuys, Bloem, & Groothuis, 1995), implicating the influence of current mood state on
cognition. On the other hand, Ridout, Noreen et al. (2009) provided evidence that naturally
occurring dysphoria and induced negative mood are associated with opposite affect identification
patterns. Specifically, their findings suggested that natural dysphoria may impair accurate
identification of sadness, while transient induced sadness may facilitate sadness recognition.
Abnormal facial affect identification may also correspond with schemas indicative of
social maladjustment (Reeves & Taylor, 2007; Young, 1990). To examine this possibility,
Csukly and colleagues (2011) had depressed inpatients complete a facial affect identification task
and the Young Schema Questionnaire, which assesses for core cognitions related to unmet needs.
These authors found that poor recognition of happiness correlated with cognitive themes of
impaired autonomy and social disconnection, poor recognition of sadness correlated with themes
of impaired interpersonal limits and social disconnection, and poor recognition of fear also
correlated with themes of impaired autonomy. The authors also noted a negative correlation
between psychiatric distress levels and affect discrimination performance, implicating possible
influences among depression severity, facial affect identification, and dysfunctional perspectives
on negotiating social environments.
Processing Biases 35
Evaluative summary. Patterns in facial affect identification findings are intertwined
with methodological variables such as stimulus affect ambiguity, stimulus presentation duration,
and sample depressive severity. Eight of 10 studies of depression susceptible groups have shown
intact, unbiased identification of unambiguous positive or negative affects presented for long
durations (but see Ridout, Noreen et al., 2009). Presumably, some depressed individuals
effectively identify static, prototypical affect in dyadic interactions. However, research suggests
that depression may also confer sensitivity to subtle sadness (found in 4 of 6 relevant studies)
and fear (in 1 of 4 studies), insensitivity to subtle happy affect (in 4 of 6 studies), and with acute
clinical severity, less accurate affect discrimination (found in 3 of 4 inpatient samples). In more
complex social interactions, displayed affects may be dynamically brief, subtle, and problematic
for depressed individuals. Such displays could overtax slowed evaluative processing resources,
leading to incomplete or mistaken perceptions. Ambiguous affective displays might evoke
negative cognitive interpretive biases. In this way, depressotypic cognitive deficits and cognitive
biases could both alter identification of facial affect. In practice, depression prone individuals
may overestimate the intensity of negative affect in others’ facial expressions and may
insufficiently differentiate subtle affects.
Importantly, depressotypic affect identification has been linked to rumination, prolonged
course, and increased relapse risk (Bouhuys et al., 1999). Although abnormal facial affect
identification has yet to predict new onsets of depression, presented research suggests that it
could be part of a theoretically viable vulnerability factor or maintenance factor. It remains
unclear whether affect identification biases or impairments are discrete trait or state effects.
Several important propositions emerge from the reviewed studies. First, findings of
depressive interpretive biases are conceptually consistent with cognitive models of depression,
Processing Biases 36
which implicate negative construal of incoming information in the development and maintenance
of depression (e.g., Beck, 1967). Second, regarding interpersonal theories (e.g., Coyne, 1976;
Lewinsohn, 1974), stable negative interpretation of others’ default, modal facial expression—
neutral affect—could shift one’s construal of social environments to be disproportionately
unpleasant. Similarly, impaired facial affect decoding presumably could lead to ineffective social
conflict resolution and confusing, dissatisfying social interactions (Csukly et al., 2011). If so,
impaired affect identification might contribute to deficient social skills, interpersonal rejection,
and asocial behavior (Depue & Collins, 1999; Greimel et al., 2010; Leppӓnen & Hietanen, 2001;
Meyer & Kurtz, 2009; Santos, Silva, Rosset, & Deruelle, 2009). However, possible connections
between abnormal facial affect processing and social dysfunction have not been sufficiently
studied in depression vulnerable groups. In support of the cognitive-interpersonal model,
dysfunctional social schemas that have been linked to affect identification difficulties (Csukly et
al., 2011) also appear related to adverse interpersonal experiences in childhood (e.g., Lumley &
Harkness, 2007). Moreover, Pollak and colleagues (2000) have established a connection between
childhood neglect (a depression risk factor) and impaired facial affect discrimination.
Additionally, the tendency to interpret ambiguous facial expressions as sad, disgusted,
fearful, or rejecting (Hale, 1998) is conceptually consistent with a depressive social risk-averse
strategy (Allen & Badcock, 2003). Accordingly, individuals in a dysphoric mood less frequently
trust others’ facial affect as genuine and they more accurately detect deception (Forgas & East,
2008a; 2008b; Lane & DePaulo, 1999). This might steer them away from unpredictable
interactions and peer victimization. In contrast, depressotypic interpretation biases that endure
even when depression has remitted (e.g., Bouhuys et al., 1999; Milders et al., 2010) contradict
the premise that the social risk-aversion strategy is mood state dependent. Lastly, at a practical
Processing Biases 37
level, studies that enroll depression vulnerable groups to examine attention and memory for
facial stimuli will need to account for interpretive biases as a potential confounding variable.
____________________________
Insert Table 1 approximately here
____________________________
Selective Attention for Facial Affect
Attention to sad affect. As catalogued in Table 2, several studies have shown a
depressive bias toward sad faces.
Depressed samples. Gotlib, Krasnoperova et al. (2004) examined whether attentional
biases to specifically depressive facial stimuli would be associated with depression and not
anxiety. Sample groups with major depression, with generalized anxiety disorder, or without
psychiatric illness completed a dot-probe task that included happy, sad, angry, and neutral facial
stimuli. At stimulus presentation durations thought to evoke attention engagement processing
(e.g., 1000 ms), the depressed group showed an attentional bias exclusively to sad faces. By
comparison, the anxious and healthy comparison groups attended equally across affects. Thus,
findings supported the content specificity hypothesis of depressive attentional bias toward
depressive information (Beck, 1976; Ingram et al., 1998). Gotlib, Kasch, et al. (2004) replicated
these specificity findings in depressed individuals, this time with a comparison group diagnosed
with social phobia. Subsequent replications provided additional evidence that an attention bias
for sad facial affect is found reliably and specifically in depression (Fritzsche et al. 2009;
Joormann & Gotlib, 2007). This conclusion is strengthened by the fact that these four studies
excluded individuals with comorbid anxiety disorders from their depressed samples—unlike
most studies examining facial affect processing and depression. Functional MRI evidence
Processing Biases 38
suggests that a depressive attentional bias toward sad affect could be supported by increased
activity in the right fusiform gyrus, left putamen, amygdala, and parahippocampal gyrus
(Surguladze et al., 2005; Suslow et al., 2010; Victor et al., 2010).
In addition, research has begun to assess whether depression is associated with biased
inhibitory processing of facial affect, as this could influence selective attention and memory.
Goeleven, De Raedt, Baert, and Koster (2006) utilized a negative priming paradigm to compare
the inhibitory processing of currently depressed and healthy control groups. As is typical, the
control group demonstrated functional negative priming (e.g., happy prime distracters slowed
subsequent responses to happy probe targets) for both valences. In contrast, despite showing
normal negative priming on happy experimental probe target trials, the currently depressed group
lacked negative priming on sad experimental probe target trials. This finding suggests that
depression may be associated with deficient attentional inhibition of peripheral sad facial affect
in the social environment.
In contrast, two studies have found no depressive attentional bias. Mogg, Millar, and
Bradley (2000) had depressed individuals complete a facial dot-probe paradigm while
monitoring directional gaze, but neither reaction time nor eye movement data indicated any
attentional bias to sad faces. However, nearly all of the depressed individuals in the Mogg et al.
sample were experiencing comorbid generalized anxiety disorder, limiting the generalizability of
this particular finding. Karparova, Kersting, and Suslow (2005) investigated whether a depressed
group would exhibit delayed disengagement of attention from negative faces compared to a
nondepressed group. All participants completed the face-in-the-crowd task twice, corresponding
with the depressed individuals’ pre- and post-treatment time points. However, at both time points
there were no between-group differences in the ability to disengage attention from distracting
Processing Biases 39
backgrounds of negative faces. It is worth noting that the schematic faces in Karparova et al.
(2005) differed only by the ends of the mouth line, a coarse, low-level feature. Thus, it is
possible that more complex spatial features comprising true emotional faces could elicit stronger
sustained attention and provide more of a challenge for disengagement processing.
At-risk samples. Several studies have explored whether attentional biases to sad facial
affect exist in populations that are at-risk but not currently depressed. Joorman and Gotlib (2007)
compared the performance of formerly, currently, and never depressed participants on a facial
dot-probe paradigm without manipulating mood. Results showed that both currently and
formerly depressed groups selectively engaged attention with sad faces. Conversely, the healthy
control group shifted attention away from sad faces and oriented toward happy faces. This study
both replicated prior findings with currently depressed individuals (Gotlib, Kasch et al., 2004;
Gotlib, Krasnoperova et al., 2004) and uniquely showed evidence of a cognitive marker that
persists beyond remission from depression (see Fritzsche et al., 2009 for replication of Gotlib &
Joormann, 2007). This contrasts with most cognitive vulnerability research evidence that
depressive cognition emerges only within a depressed mood state (Scher et al., 2005).
In another study of an at-risk group, Hsieh and Ko (2004) explored whether increased
levels of trait-depression were associated with facial affect attentional biases. The study’s
college student sample completed a deployment of attention task and a self-report personality
inventory that assessed stable trait depression. A median split on trait depression formed two
groups for comparison. Because clinical interviews were not conducted, some high trait-
depressed participants might have met criteria for major depression. But given that the one-year
prevalence rate of major depression approximates 4.1% internationally (Waraich, Goldner,
Somers, & Hsu, 2004), a larger proportion of the above-median trait-depression group may have
Processing Biases 40
been experiencing less-severe dysthymia or dysphoria (Ingram & Hamilton, 1999), clinically
significant phenomena that portend greater risk of major depression. Although high trait-
depression participants attended more to sad faces than low trait-depression participants, this
effect was mostly driven by the tendency for low trait-depression participants to inhibit
attentional engagement with sad faces. Thus, the at-risk group lacked normal attentional
inhibition of others’ sad facial affect.
Goeleven and colleagues (2006) utilized a negative priming procedure to examine
attentional inhibition of facial affect in currently euthymic individuals with a history of recurrent
depression. In contrast to currently depressed and never depressed groups (reviewed earlier), the
formerly depressed group showed impaired inhibition for both sad and happy facial affect. This
surprising result might suggest that multiple depressive episodes sensitize one’s attention to
generalized facial affect in the social environment. An alternative speculation stems from the
finding that greater recurrence tends to correspond with increasingly heritable forms of
depression (Sullivan, Neale, & Kendler, 2000). More heritable forms of depression have been
associated with greater baseline amygdala activity, but less reactivity to affective stimuli,
including faces (Drevets, 2000; Wolfensberger et al., 2008). Such a profile could potentially be
genetically-mediated (e.g., MAOA-H risk allele) via decreased prefrontal-amygdala
connectivity, which would hinder adaptive feedback and regulation in response to emotional
stimuli (Dannlowski et al., 2009). In a negative priming task, decreased prefrontal-amygdala
connectivity might lead to insufficient removal of irrelevant affective content from working
memory. This deficiency might be most prominent in the genetically vulnerable person who is
experiencing depression, when sad distracters would be mood-relevant and unlikely to elicit
robust attentional inhibition. When the same person is not depressed, sad content is no longer
Processing Biases 41
mood-congruent, so deficient inhibition might appear equivalent for sad and happy facial affect.
If Goeleven et al. (2006) represents a reliable effect, differing levels of recurrence and/or
heritability within depression susceptible samples might contribute to heterogeneous results in
studies examining potential depressive cognitive biases (see also Gilboa-Schechtman, Ben-Artzi,
Jeczemien, Marom, & Hermesh, 2004).
The aforementioned research has shown that attentional biases are not just correlates of
major depression, but these studies did not assess whether biases arise in advance of first
depressive onsets. In a study with this objective, Joormann, Talbot, and Gotlib (2007) explored
whether young, euthymic, never depressed daughters of mothers with a history of recurrent
depression would show negative attentional biases to facial affect, compared to girls of never
depressed mothers (high- and low-risk). Participants completed a sad mood prime and then a dot-
probe task consisting of happy, neutral, and sad facial stimuli. The high-risk group showed a bias
toward sad faces but not happy faces. In contrast, the low-risk group showed a bias toward happy
faces but not sad faces. Interestingly, Gibb et al. (2009) found that without a sad mood induction,
children of mothers with a history of depression avoided attending to sad faces but not happy or
angry faces. Avoidance was strongest in children with putative environmental stress-sensitivity
(5-HTTLPR S or LG allele carriers), whose elevated level of dysphoria correlated highly with
their mothers’. Notably, the at-risk sample included some formerly depressed children, endorsed
varying levels of current dysphoria, and was mixed-sex, all of which differed from Joormann et
al. (2007). Nonetheless, these studies indicate that when not acutely sad, some children of
depressed mothers try to regulate their affect by ignoring sad faces; when sad, their attention
might be drawn to sad faces and lack a potentially mood-enhancing bias toward happy faces. If
Processing Biases 42
research can demonstrate that these response patterns are influenced by suboptimal attachment,
such findings would support the cognitive-interpersonal model.
Attention to happy affect. Several studies have reported that depression susceptible
groups attend less to positive faces.
Depressed samples. Suslow and his collaborators have examined attentional capture of
facial affect via the face-in-the-crowd task. Suslow, Junghanns, and Arolt (2001) compared the
performance of clinically stabilized depressed inpatients and nondepressed individuals. These
investigators found that the patient group was significantly slower to orient and engage attention
with positive faces than their counterpart group, even after controlling for trait anxiety. To
examine potential treatment effects on this bias, Suslow et al. (2004) assessed face-in-the-crowd
performance of depressed patients (half of whom were diagnosed with comorbid anxiety
disorders) before and after six sessions of psychotherapy. The authors compared this depressive
performance profile with that of nondepressed individuals at parallel time points. Despite
evincing clinically significant symptom improvements, the comorbid depressed group (but not
the depressed group without comorbidity) more slowly detected positive faces than the
nondepressed group at both time points. Between depression and anxiety, only depression has
been reliably linked to deficits in serial, effortful processing (Hartlage, Alloy, Vázquez, &
Dykman, 1993), which is required for detecting happy faces (White, 1995). Thus, it is unclear
why the comorbid group showed slowed processing and the non-comorbid depressed group did
not. One explanation is that the comorbid group possessed more severe psychopathology (e.g.,
average BDI was 5 points greater than the non-comorbid depressed group), and severity is
related to impaired processing of positive affect. Consistent with this explanation, Gotlib, Kasch,
Traill, Joormann, Arnow et al. (2004) found that, within a depressed group, greater symptom
Processing Biases 43
severity was associated with greater attention away from happy faces. Fritzsche et al. (2009)
found a similar result in their depressed sample.
Reduced attentional processing should be measurable in corresponding neural activity. To
this end, Cavanagh and Geisler (2006) examined the parietal P300 ERP response of depressed
and nondepressed students on an oddball task that presented alternating blocks of rare happy and
fearful target faces interspersed among frequently presented neutral faces. The investigators
found that, compared to the control group, the depressed group showed reduced mean P300
amplitude to happy target faces but not to fearful ones. In line with this finding, neuroimaging
research has revealed links between depression and reduced cortical activity in parietotemporal
and prefrontal areas, as well as in the insula. Reduced attentional engagement could also result
from hypoactivity in subcortical structures that process socially rewarding stimuli. Depression
has been associated with reduced amygdala, hippocampus, putamen, and caudate activity in
response to viewed positive facial affect (see Domschke et al., 2008; Schaefer, Putnam, Benca,
& Davidson, 2006; Surguladze et al., 2005 fMRI studies).
Still, other research suggests that depression can be associated with the lack of a normal
positive attentional bias that would otherwise promote positive moods and social engagement.
Joormann and Gotlib (2007) found that a currently depressed group lacked the tendency shown
by a never depressed group to bias attention toward happy faces. Likewise, depressed groups
may lack the tendency shown by healthy groups to allocate greater neural processing resources to
happy faces than sad faces (Deldin et al., 2001; Deveney & Deldin, 2004; Victor et al., 2010). In
contrast, several studies have reported no difference between depressed and nondepressed groups
in attention to positive facial affect (Gotlib, Kasch et al., 2004; Gotlib, Krasnoperova et al., 2004;
Karparova et al., 2005; Mogg et al., 2000).
Processing Biases 44
At-risk samples. Groups at risk of depression have also shown less attention to positive
facial affect than low-risk groups. Two studies have indirectly examined whether dysphoria
might impact attention to mood-congruent or mood-incongruent affective faces. In Bradley and
colleagues (1998) study, high and low trait anxiety groups performed a dot-probe task that
included happy, threatening and neutral facial stimuli. Although the hypothesized bias toward
threatening stimuli was found in highly anxious individuals, dysphoria was found to be
correlated with a bias away from happy faces relative to neutral faces. Bradley and colleagues
(2000) replicated this result in sample grouped by low, medium, and high levels of state anxiety.
Additionally, at-risk individuals may lack a normal attentional bias toward positive facial
affect. Joormann and Gotlib (2007) found that a formerly depressed group lacked the positive
attentional bias that a never depressed group exhibited. Joorman, Gotlib, and Talbot (2007) also
found a parallel difference between never depressed girls with maternal depression history and
never depressed daughters of never depressed mothers. However, Hsieh and Ko (2004) found
that high-risk and low-risk groups were no different in processing positive facial affect.
One study suggests that at-risk groups may insufficiently filter affective information,
particularly happy affect, when they process others’ faces. Gilboa-Schechtman et al. (2004)
compared dysphoric and nondysphoric groups’ ability to focus on a nonemotional dimension of
facial expressions while ignoring the potentially distracting emotional dimension. Participants
completed a Garner speeded-classification task in which they were asked to identify, quickly and
accurately, the gender of actors displaying happy, angry, and neutral affects. The degree to which
each emotion slowed gender-labeling was a measure of attentional interference. Results indicated
that the dysphoric group showed greater interference than the nondysphoric group for all affects,
Processing Biases 45
and showed greater interference from happiness than from anger. Therefore, some—but not all—
evidence suggests that depression susceptibility evinces reduced attention to positive faces.
Attention to angry and fearful affect. Theory and evidence suggest that depression
could be associated with attentional biases toward other negative affects, such as anger, fear, and
disgust (Allen & Badcock, 2003; Beevers et al., 2009).
Depressed sample. Leyman, De Raedt, Schacht, and Koster (2007) examined the
performance of currently depressed and nondepressed individuals on an exogenous cueing task
that used neutral and angry facial stimuli. Both groups showed facilitated early attention for
angry affect. However, the depressed group’s attentional engagement and maintenance with
angry faces was greater relative to neutral faces and relative to the nondepressed group, who
more rapidly shifted attention away.
At risk sample. When asked to view faces for 12 seconds each, a dysphoric group showed
broader visual attentional scanning of angry faces relative to other affective faces and relative to
a nondysphoric group (Wells, Beevers, Robinson, & Ellis, 2010). These results may or may not
correspond with depressive attenuated orbitofrontal activity in response to angry affect (Lee et
al., 2008). Conversely, several studies have indicated that dysphoric and depressed groups’
attention to angry facial affect was no greater than for other affects and indistinguishable from
nondepressed groups (Gilboa-Schechtman et al., 2004; Gotlib, Kasch et al., 2004; Gotlib,
Krasnoperova et al., 2004; Hsieh & Ko, 2004; Koster et al., 2006). Similarly, depressed
individuals have shown normal attentional processing of fearful facial affect.
Evaluative summary. Attention biases to facial affect are relatively reliable correlates of
major depression, and cognitive neuroscience has begun to illuminate possible
neuropsychological mechanisms of these biases (Cavanagh & Geisler, 2006). Three of four
Processing Biases 46
studies examining the orienting phase of attention (e.g., stimulus presentations of 500 ms) have
shown decreased orienting to happy faces in depression prone groups. In five studies, these
groups have shown biases away from positive affect toward other affects. In two other studies
depression prone groups have lacked a bias toward positive affect that was observed in healthy
control groups. When biases away from positive affect have been found, they have consistently
occurred in groups experiencing stable dysphoric mood. In contrast, a lack of a positive bias has
been observed in one currently depressed group, in one never-depressed group with familial
depression risk, and in one nondysphoric formerly depressed group (Joormann & Gotlib, 2007;
Joorman et al., 2007). If more research finds that nondysphoric at-risk individuals lack a
normative attention bias toward positive facial affect, such a processing tendency might reflect
an enduring trait marker of risk.
Six of nine studies that assessed attentional engagement and maintenance (e.g., 1000 ms
presentations) found greater maintained engagement with sad faces in depressed groups.
Deficient inhibition of sad irrelevant facial affect in the social environment might contribute to
such a depressive attentional bias (Goeleven et al., 2006; Hsieh & Ko, 2004). Null results have
emerged from a smaller depressed sample (Mogg et al., 2000) and from a study that tracked
visual attentional gaze (Wells et al., 2010), while a bias away from sad affect was found in an at-
risk child sample (Gibb et al., 2009). Although the content specificity hypothesis has been
generally supported, bias toward angry affect was found in two of seven relevant studies
(Leyman et al., 2007; Wells et al., 2010). Thus, attentional biases to negative facial affect have
been frequently found in depression. However, only studies of at-risk samples can examine
whether attentional biases precede depressive onsets and impact the etiology of depression.
Processing Biases 47
Results with depression-vulnerable groups mostly match results found with currently
depressed groups. In particular, at-risk groups have shown deficient orienting to happy affect in 2
of 3 studies and greater attentional maintenance with sad affect in 4 of 6 studies. Importantly,
longitudinal evidence suggests that attention deficits for positive facial affect can persist even
after depression remits (Suslow et al., 2004). Further, depression-susceptible groups appear to
insufficiently inhibit attention to viewed sad (Goeleven et al., 2006; Hsieh & Ko, 2004) and
happy facial affect (Gilboa-Schechtman et al., 2004; Goeleven et al., 2006). Perhaps due to the
emotional discomfort others’ sadness might cause them (Persad & Polivy, 1993), some
vulnerable children direct their attention away from sad faces (Gibb et al., 2009).
Relevant to cognitive theories of depression, findings from studies that have examined
attention with emotional facial stimuli largely parallel studies that have used verbal stimuli.
Depression susceptible groups tend to allot greater attention toward negative words (e.g.,
Bradley, Mogg, & Lee, 1997; Karparova, Kersting, & Suslow, 2007), to lack mood-buffering
avoidance or inhibitory processing of negative words (e.g., Joormann, 2004; MacLeod,
Mathews, & Tata, 1986), and to exhibit deficient engagement with positive words. Although
depression has been linked to impaired disengagement from negative words (Koster et al., 2005),
impaired disengagement from sad faces has not been reported. Also, it remains unclear whether
past depression, in the absence of dysphoric mood, is linked to either an attentional inhibition
deficit for facial affect in general or specifically for negative affect. Overall, however, studies
using affective facial stimuli have provided complementary support for cognitive theories.
Interestingly, attending to negative faces can instigate a depressive mood-maintaining
internal monologue. In a clever study, Frewen and Dozois (2005) asked women to: (a) identify
the affect of each presented happy, sad, fearful, disgusted, or angry face; (b) imagine they had
Processing Biases 48
just seen the person; and (c) characterize their own subsequent automatic thoughts. Though both
groups accurately identified all affects, the dysphoric women were more likely than
nondysphoric women to blame themselves for others’ negative affects and to ascribe external
causes for others’ positive affect. Also, dysphoric women more often interpreted others’ negative
affects to reflect a negative evaluation of them. Lastly, after viewing sad, angry, disgusted, or
happy faces, dysphoric women were more likely to endorse negative thoughts about themselves.
Possibly related, Fritzsche et al. (2009) found that depression prone groups who preferentially
attended to sad facial affect also recalled more negative self-referent adjectives they had
previously endorsed, compared to a never depressed group who showed neither pattern. In
addition, Persad and Polivy (1993) found that depressed and dysphoric groups endorsed elevated
sad emotional responses to negative and positive affective faces they viewed. Thus, greater
attention to negative facial affects might evoke immediate heightened depressotypic cognitive
and emotional responses, possibly giving rise to the kind of ruminative processing that prolongs
depressed moods (e.g., Nolen-Hoeksema et al., 1993). Although depressotypic attentional biases
have appeared in never depressed at-risk individuals (Joormann et al., 2007), it is unknown
whether these biases predict future depressive onsets, as the combination of negative cognitive
style and stress-reactive rumination has (Robinson & Alloy, 2003).
The reviewed research also highlights plausible connections between attention findings
and interpersonal, cognitive-interpersonal, and social risk theories, which warrant further
empirical testing. Regarding interpersonal theories, attention bias toward others’ sad affect could
serve as a particularly aversive consequence (Persad & Polivy, 1993) that inhibits assertive social
behavior or as a sobering reminder to cling to close relationships (Barnett & Gotlib, 1988;
Lewinsohn, 1974). Per cognitive-interpersonal theory, non-depressed children who have
Processing Biases 49
experienced depressive parenting have shown abnormal attention to sad affect (Gibb et al., 2009;
Joormann et al., 2007). Similarly, heightened emotional reactivity and dysphoria-promoting
patterns such as self-criticism and perceived negative evaluation by others can originate from
suboptimal interpersonal experiences (Hammen & Gotlib, 1993). Consistent with the social risk
model, a depressive tendency to assume blame for others’ misery and to take no credit for others’
happiness (e.g., Frewen & Dozois, 2005) seems to exemplify a mode of low perceived social
investment potential (i.e., high burden and low value to others; Allen & Badcock, 2003). In
tandem, vigilant attention to others’ sad, angry, and disgusted affect could regularly restore a
depressive cognitive-emotional mode, which presumably guides risk-averse behavior.
Irrespective of theoretical perspective, the reviewed attentional research suggests that distinct
patterns of thought and emotion occur when depression susceptible individuals encounter
affective expressions in their daily lives.
___________________________
Insert Table 2 approximately here
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Memory for Facial Affect
Studies indicate that depressed and depression susceptible groups can show abnormal
recognition memory for faces displaying sadness, happiness, anger, or fear.
Memory for sad affect.
Depressed samples. Ridout et al. (2003) asked depressed and nondepressed individuals to
complete a facial emotion identification task and then a delayed recognition memory task (see
Table 3 for listing of memory studies). Although both groups categorized the emotional faces
similarly at presentation, groups differed in the types of faces they recalled. The depressed group
Processing Biases 50
recognized more sad faces and fewer happy faces compared to neutral faces. The nondepressed
group showed better memory for happy faces and worse memory for sad faces.
In another study that explored recognition memory for faces, Gilboa-Schechtman et al.
(2002) included a group with comorbid depression and anxiety, a group with non-comorbid
anxiety, and a group without psychiatric illness. Participants first completed an incidental
encoding task with sad, angry, happy, and neutral facial expressions (i.e., rating whether they
would like to meet the photographed person). On this task, both patient groups reported less
willingness to meet people who displayed angry or sad affect than the control group. Later,
participants were asked to identify only the faces of people they had seen previously, although
these familiar people displayed different affects in the recognition phase than in the encoding
phase. On this task, the comorbid group recalled more actors who had previously conveyed
angry and sad faces compared to happy and neutral facial expressions. The control group
exhibited the opposite pattern of memory bias.
Ridout, Dritschel et al. (2009) also examined whether major depression would be
associated with memory biases for sad facial affect following an incidental encoding task.
During the encoding phase, depressed and healthy never depressed participants viewed affective
facial expressions and identified each actor’s gender. Next, participants completed a recognition
memory test. Contrary to initial predictions, the depressed group showed no biased recognition
of sad faces. Based on these findings, mood congruent memory biases for affective faces might
only emerge if emotion is processed explicitly during encoding (Ridout, Dritschel et al., 2009).
Interestingly, this rationale could also explain results from Gilboa-Schechtman et al. (2002).
Specifically, when considering whether one wants to meet a person in a photograph, she or he
might explicitly process facial affect cues for friendliness and dominance. As Gilboa-
Processing Biases 51
Schechtman et al. (2002) found, this encoding task should evoke in depressed individuals a
subsequent bias for recognizing more persons who had displayed sad affect. In contrast,
discriminating gender requires no explicit processing of facial affect and should evoke no
memory bias.
Deldin et al. (2001) utilized ERP techniques to investigate links between depression and
recognition memory of affective faces. These investigators compared parietal P300 amplitudes of
depressed and nondepressed individuals during incidental encoding and subsequent recognition
of positive and negative faces. The authors found that the nondepressed group showed greater
P300s during encoding and attenuated P300s when recognizing positive faces compared to
negative faces. In contrast, the depressed group showed no differential processing allocation
between positive and negative faces or between encoding and recognition memory phases.
Therefore, relative to the pattern of nondepressed individuals, depressed individuals tended to
allocate lesser processing resources to encoding positive expressions and greater resources to
recognizing negative expressions. Interestingly, the study did not find behavioral differences in
recognition memory. This could indicate that the brain can recruit compensatory neural resources
to minimize impairment in performance (Drummond et al., 2001). Lastly, the P300 ERP
component can provide a snapshot of attentional processing (e.g., context updating; Donchin &
Coles, 1988) during encoding and retrieval. However functional neuroimaging, which can track
and measure ongoing elaborative processing, will likely provide important additional knowledge
about depressive memory biases.
At-risk samples. Three studies have examined memory for sad facial affect in dysphoria.
Hsieh and Ko (2004) asked high and low trait depression participants to identify only affective
faces they had seen previously in an attention task. On this recognition memory task, the high
Processing Biases 52
trait depression group showed significantly greater memory strength for sad faces than the low-
risk control group. No between-group memory differences were found for angry, happy, or
neutral faces.
Jermann et al. (2008) utilized a remember/know/guess paradigm to compare dysphoric and
nondysphoric groups’ performance on recognizing the identity and affect of previously presented
faces. Participants first viewed images of sad and happy facial expressions of actors. Later in the
recognition phase, they viewed neutral expressions of old and new actors, and were asked to
identify only those actors who were presented in the encoding phase. Dysphoric individuals
showed a greater proportion of accurate ―remember‖ responses for actors presenting sad facial
expressions than nondysphoric individuals. In this study, encoding was intentional, and the only
recall bias to emerge was conscious recognition of both affect and identity.
Recently, Ridout, Noreen et al. (2009) conducted experiments examining possible mood
congruent memory biases for facial affect in naturally occurring dysphoria and in induced
moods. In both experiments, participants completed a delayed recognition memory task
following an incidental encoding (affect identification) task that included sad, happy, and neutral
facial stimuli. In the first experiment, the naturally dysphoric group recognized significantly
more sad faces relative to neutral and happy faces. They also recognized significantly more sad
faces and fewer happy faces than the nondysphoric group. In the second experiment, researchers
induced negative or positive mood in healthy, euthymic participants and examined their
subsequent information processing. Parallel to the dysphoric group in the first experiment, the
negative mood-induced group recognized significantly more sad faces, relative to happy and
neutral faces, and in comparison to the positive mood-induced group. Unlike the between-group
contrast in the first experiment, the negative mood group did not recognize fewer happy faces
Processing Biases 53
than the positive mood group. Thus, naturally occurring and induced mood may affect memory
differently, an important nuance in the conceptualization of mood-congruent cognition.
Memory for happy affect.
Depressed samples. Depression can also be characterized by decreased memory for
positive facial affect. For example, Ridout et al. (2003) found that a depressed group recognized
fewer happy faces than a nondepressed group. The depressed group also recognized fewer happy
faces than neutral or sad faces. In other studies, depressed groups have shown no bias, while
their nondepressed counterparts showed preferentially increased memory for positive faces
(Gilboa-Schechtman et al., 2002; Ridout et al., 2003). Aligned with these results, Deldin et al.
(2001) found that a nondepressed group allocated more cognitive resources to encoding happy
faces and less to recalling them. Their depressed group showed no differential allocation between
encoding and recall. Theoretically, greater encoding promotes more efficient retrieval. In this
way, depression could reduce encoding of positive affect and thereby limit its accessibility in
stored memory.
At-risk samples. Thus far, a memory deficit for positive facial affect has not been found in
groups susceptible to depression (Hsieh & Ko, 2004).
Memory for angry and fearful affect.
Depressed sample. Although an increased memory bias for angry facial affect was found
in a group with comorbid depression and anxiety (Gilboa-Schechtman et al., 2002), no findings
from non-comorbid depressed samples have been reported.
At-risk samples. Groups with above average trait depression or parental history of
depression have shown no memory bias for angry faces (Hsieh & Ko, 2004; Pine et al., 2004).
However, in one study, a stable dysphoric group more accurately recalled angry faces after a 15-
Processing Biases 54
minute delay than a nondysphoric group (Wells et al., 2010). Importantly, broader visual
attention during prolonged face viewing (i.e., incidental encoding) mediated this effect.
However, with only a brief encoding period, a dysphoric group held angry faces in working
memory no more strongly than a nondysphoric group (Noreen & Ridout, 2010). As such,
attentive, elaborated encoding may be necessary to cultivate long-term memory biases for angry
faces. Regarding fearful affect, Pine and colleagues found that a formerly depressed adolescent
group showed reduced memory for fearful faces compared to a never depressed group.
Evaluative summary. Depressed and depression susceptible groups have recalled more
sad faces than control groups in three studies and have recalled more sad faces than happy faces
in two other studies (but see Deldin et al, 2001; Ridout, Dritschel et al., 2009; Wells et al., 2010
for null findings). These biases might be partially mediated by relatively increased or decreased
cortical processing, respectively (e.g., Cavanagh & Geisler, 2006; Deldin et al., 2000). Also,
comorbid anxiety or prolonged attentional engagement with others’ faces may lead to a memory
bias for angry affect (e.g., Gilboa-Schechtman et al., 2002; Wells et al., 2010).
Current emotional facial expression recognition memory findings in depressed and
depression vulnerable groups fit most strongly with cognitive models of depression, which
predict greater memory access to depressive information. By extension, these findings are also
consistent with the more broadly developed literature using verbal information (e.g., words,
sentences, idea units) on explicit and implicit memory tasks. Specifically, explicit recall and
implicit priming biases for depressive verbal information have been found in depressed and
depression vulnerable individuals (Matt et al., 1992; Watkins, 2002). Additionally, most results
have been in line with the content specificity hypothesis (e.g., Hsieh & Ko, 2004; Jermann et al.,
2008; Ridout et al., 2003; Ridout, Noreen et al., 2009). However, two of five studies that have
Processing Biases 55
examined possible memory biases for angry affect have found them. These findings partially
support the social risk hypothesis. Presumably, memory for socially aversive people, when
paired with a motivation to avoid such people (e.g., Gilboa-Schechtman et al., 2002), would aid a
risk-averse strategy.
Important questions relevant to cognitive theories remain to be investigated. First, do at-
risk individuals in a nondysphoric or dysphoric mood exhibit memory biases for mood-relevant
affective facial expressions? Second, are there differential effects of incidental and conscious
encoding of facial affect on memory storage and implicit or explicit retrieval? Because cognitive
schemas are thought to have limited conscious accessibility, the current collection of studies
examining explicit memory retrieval represents only a partial empirical exploration of cognitive
theories. Preliminary results suggest that explicit processing may be necessary for explicit
recognition memory biases to emerge (e.g., Gilboa-Schechtman et al., 2002; Ridout et al., 2003;
Ridout, Dritschel et al., 2009). However, it will be important for future research to examine
depressive implicit memory for relevant facial affect following explicit encoding of affect. Such
an approach may increase external validity, as conscious awareness and external motivation may
be absent when people access affective associations in naturalistic social settings.
____________________________
Insert Table 3 approximately here
____________________________
Discussion
Although depressive cognition has historically been investigated using verbal stimuli,
others’ facial affect should be particularly important in depression (Allen & Badcock, 2003;
Processing Biases 56
Gotlib, Krasnoperova et al, 2004). We have described information processing studies that use
emotional facial stimuli and have begun to evaluate findings within the frameworks of cognitive,
interpersonal, cognitive-interpersonal, and social risk models of depression. The literature
indicates that depression susceptible groups often show attentional, interpretive, and memory
biases regarding depression-relevant facial affect.
Affect identification
Depressed individuals tend to recognize static, unambiguous facial affect without bias
(e.g., Segrin, 2001), but they tend to interpret ambiguous affective facial expressions more
negatively than do nondepressed individuals (e.g., Gollan et al., 2008). Interpretive biases may
be an important factor in the assessment of treatment outcome (Merens et al., 2007; Venn et al.,
2006) and risk of relapse, as some symptomatically remitted individuals might continue to
perceive social interactions negatively and be more vulnerable to relapse (Bouhuys et al., 1999).
More-severe depression can be characterized by increased sensitivity to various negative affects
but decreased specificity in discriminating among them. Such a pattern has been associated with
increased rumination and decreased prosocial behavior (Marsh et al., 2007; Raes et al., 2006).
Importantly, we did not encounter this sensitivity/specificity pattern in depression research that
used emotional word stimuli or non-facial picture stimuli.
Attention
Depression and depression vulnerability have frequently been associated with early
deficits in orienting to happy faces, which may persist even after depressive symptoms remit
(Suslow et al., 2004). In addition, depression susceptibility has been reliably linked to increased
attentional engagement with sad faces. Depression may also be characterized by a deficit for
inhibiting attention to negative facial emotion (Goeleven et al., 2006). Future research should
Processing Biases 57
evaluate the hypothesis that abnormal cognitive inhibition influences biased attention and
memory processing (Linville, 1996). Also, comorbid depression and anxiety may be
characterized by increased attention and memory for angry affect (Gilboa-Schechtman et al.,
2002; Leyman et al., 2007). This fits with evidence that anxiety confers sensitivity to threatening
information (Bradley et al., 1998; Fox, Russo, Bowles, & Dutton, 2001).
Attention findings are mostly consistent with affective verbal research supporting
cognitive models of vulnerability (Scher et al., 2005). However, select attentional findings with
verbal stimuli have not been replicated with facial stimuli (Karparova et al., 2005), vice versa
(Deldin et al., 2000). Similarly, whereas depressotypic biases have been proposed and found to
occur at later elaborative stages of processing (Bradley et al., 1997; Williams et al., 1997),
cognitive neuroscience evidence indicates that attention and interpretation biases for facial affect
begin during pre-conscious, automatic processing (e.g., Csukly et al., 2011; Suslow et al., 2010).
Studies that examine both verbal and facial affect processing in a single depressed sample are
rare and are needed to help answer an important question: Do depressotypic cognitive biases
generalize across modalities, or can biases for facial affect be dissociated from biases for
affective words? Although extant data implicate non-identical neurocognitive substrates (Deldin
et al., 2000; Vanderploeg et al., 1987), more empirical comparisons are needed.
Memory
Depression and depression susceptibility has been frequently linked to increased recall of
sad facial affect. In tandem, memory for happy affect is often abnormally deficient in depressed
groups (Gilboa-Schechtman et al., 2002; Jermann et al., 2008; Ridout et al., 2003). Less
frequently, depression susceptible groups have shown increased recognition memory for angry
Processing Biases 58
affect. Abnormal attentional processing of facial affect may modulate memory encoding and
subsequent access (Deldin et al., 2001; Ingram, 1984; Wells et al., 2010).
Integrating perspectives
Studies have assessed cognitive models of depression nearly by default, even though
findings are also relevant to the other theories of interest. Accordingly, substantial evidence
supports hypothesized cognitive processing biases. Related to this, processing facial affect can
promote negative thought patterns putatively generated from depressive schemata (e.g., Frewen
& Dozois, 2005; Fritzsche et al., 2010). Although many facial affect processing studies support
the idea of cognitive reactivity in negative mood states, negative biases have also been found in
nondysphoric depression-vulnerable samples (e.g., Bouhuys et al., 1999; Joormann & Gotlib,
2007). Regarding the cognitive-interpersonal model, impaired facial affect processing has been
linked with childhood neglect and with early maladaptive social schemas (Csukly et al., 2011;
Pollak et al., 2000). In line with interpersonal models, processing others’ facial affect appears to
be less rewarding and more aversive for depression-susceptible individuals (Domschke et al.,
2008; Persad & Polivy, 1993; Surguladze et al., 2005). Evidence for a risk-averse mechanism has
been mixed and more difficult to interpret due to operationalization issues explained later.
Cognitive neuroscience research on depression has begun to forge links between cognitive
biases and observable differences in neural activity. In particular, depressed groups viewing
depression-relevant facial affect have shown correspondingly biased activity in structures with
specialized face-processing neural networks, such as the fusiform gyrus, orbitofrontal cortex, and
amygdala (Rolls, 2008; Surguladze et al., 2005; Suslow et al., 2010; Wolfensberger et al., 2008).
Findings that connect abnormal fusiform response and common depressotypic facial affect
processing biases (e.g., Deldin et al., 2000) most strongly indicate a face-modality-specific
Processing Biases 59
element to these biases. Thus, the aforementioned biases appear to be, at the least,
neurocognitive markers of depression or depression risk.
Beyond this, however, interpretive, attentional, and memory biases present plausible
causal mechanisms. So, these biases may actually be vulnerability or maintenance factors
(Ingram et al., 1998). After initial facial affect processing, cognitive and interpersonal factors
may interact dynamically. Construing the social environment as more unpleasant, unmanageable,
and less rewarding might constrict one from pursuing social interactions with the potential to
boost mood and reinforce affiliation (Csukly et al., 2011; Depue & Collins, 1999). Also,
negatively perceived social interactions and related thoughts and feelings of low self-worth and
sadness (Frewen & Dozois, 2005; Fritzsche et al., 2009; Persad & Polivy, 1993) could fuel
aversive reassurance seeking and self-verification behaviors that might ultimately worsen
alienation and dysphoria (Joiner & Coyne, 1999; Swann, Wenzlaff, Krull et al., 1992).
Importantly, these theoretical processes need empirical support. Selective attention toward sad
faces and away from happy faces in the social environment might lead to greater sustained
depressive cognitive processing and moods, forming deeper, elaborated, depressive memory
structures (Ingram, 1984; Linville, 1996; Nolen-Hoeksema et al., 1993). As such, proportionally
greater memory for depressive interpersonal affect could also contribute to a broad negative
schema about the social world (e.g., Jermann et al., 2008). Further, in that facial expressions
elicit congruent emotions, a proportional memory deficiency for positive faces might also reduce
social approach related behaviors, a pattern characteristic of depressed individuals (e.g., Ridout,
Noreen et al., 2009). Again, empirical research is needed to test the theorized connections joining
the largely discrete cognitive and interpersonal findings.
Additional comments and recommendations for future investigation
Processing Biases 60
Emotional faces carry both valence and social (e.g., affiliation, dominance) information,
and in the reviewed studies these two types of information cannot be disentangled. For example,
the angry face of a stranger might convey high dominance and low affiliation. In this case, both
valence and social information predispose one to avoid this person. Therefore, found biases
could be toward valence information, social information, or an interaction of both. This dual
nature underscores that facial affect is not arbitrarily interchangeable with other types of valent
stimuli. Acknowledging this, the current review has emphasized the interplay between cognitive
and interpersonal factors.
The present review has also shown that methodological factors (e.g., type of task, stimulus
duration) and sample diagnostic factors (e.g., comorbid anxiety and depressive severity)
influence the findings and theoretical conclusions derived from individual studies. Studies that
test theories or fill empirical gaps left by these conditional factors are needed to expand our
understanding of abnormal facial affect processing. For instance, variability in empirical findings
(e.g., inconsistently found biases for angry faces) may reflect the known heterogeneity of
depressive presentations. Examining neurocognitive processing of facial affects in various
feature presentations of depression (e.g., agitated/hostile, atypical, melancholic, recurrent) might
clarify seemingly anomalous findings and specific diagnostic features. Given the high co-
occurrence of anxiety with depression, studies have increasingly reported sample anxiety levels,
and a few have even controlled for anxiety level in their analyses. However, most studies have
not explicitly excluded comorbid anxiety disorders or reported their presence in depressed
samples, unless comorbidity itself was a research focus. Future facial affect processing studies
should carefully document the extent to which clinically significant anxiety co-occurs with
depression in their samples. Similarly, continued research examining gene and gene-by-
Processing Biases 61
environment operationalizations of depression risk and abnormal facial affect processing (e.g.,
Dannlowski et al., 2008; Dannlowski et al., 2009; Gibb et al., 2010; Wolfensberger et al., 2008)
might account for variance and help explain potential mechanisms of risk.
Additionally, emotional valence specificity and generality findings merit refined
hypothesis formulation and testing. Some studies have found information processing biases
specific to depression-relevant facial affects, while other studies have found biases toward a
wider variety of negative—perhaps socially threatening—affects. However, more consistent
support for content specificity is provided by attention and memory studies, and to a lesser
extent, the affect identification literature. Also, greater differentiation among emotions is needed
when operationalizing the social risk hypothesis descriptor, ―socially threatening.‖ We have
presumed that this concept includes facial expressions portraying sadness, disgust, anger,
contempt, fear, and ambiguous neutrality, emotions that have distinct motivational, behavioral,
and neurobiological signatures (Bradley & Lang, 2007; Springer, Rosas, McGetrick, & Bowers,
2007). However, only a few studies have found abnormal depressive cognitive biases for anger,
fear or disgust (notably, only affect identification studies included disgust).
Sad facial affect processing more consistently distinguishes depression-prone from low-
risk groups. This could be due to the differential salience of the affect. A happy woman might
find an unfamiliar man’s sadness irrelevant, and she might sense that her mood will worsen (e.g.,
Ruys & Stapel, 2008) if she interacts with him. Positive moods promote health and social
behaviors that increase one’s potential for survival and reproduction (Pressman & Cohen, 2005).
Thus, in an evolutionary or clinical sense, relatively minimizing perception and memory of
others’ misery might actually be beneficial. In depression, a cognitive state appears to make
others’ sad affect unavoidably salient. Concomitantly reduced positive emotion may prevent one
Processing Biases 62
from reaching out to these sad others, as the social outcome would be unacceptably
unpredictable. For instance, cavorting with an outcast might worsen one’s social standing. Thus,
a risk-averse social strategy might be adaptive in the short term for a person with temporarily
depreciated social standing (Badcock & Allen, 2003; Forgas & East, 2008a, 2008b), but not in
the long term. Allen and Badcock (2003) also predict that depressed moods should direct one’s
attention away from positive reassuring information, but it is unclear what purpose this would
serve. One speculation is that a depressed man could protect himself from being deceived by
avoiding a smiling stranger, who has unknown intentions (see Forgas & East, 2008a, 2008b).
Indeed, reviewed studies exclusively used facial expressions of strangers, and not familiar
persons who might be more predictable and safe. Nevertheless, the social risk hypothesis
incisively connects traditionally cognitive concepts, such as self-worth, hopelessness, and
cognitive bias, with social status and functioning. The theory also offers a speculative
evolutionary account of a putative biasing program activated by depressed moods and
maladaptively de-regulated in major depression.
Future research that examines different aspects of information processing with multiple
methodologies will lead to a greater understanding of the processes at work in depressive
cognition. Cognitive neuroscience research will continue to examine both discrete and protracted
psychophysiological processes related to cognitive biases. Studies that assess biases on
simultaneous behavioral and neural levels of analysis will be invaluable (e.g., Dannlowski et al.,
2007a). For example, research could examine the depressive response to sad target and distracter
faces in an oddball paradigm.
Ideally, future research will employ prospective designs to test possible causal pathways.
The current, largely cross-sectional evidence base presents theoretically viable but empirically
Processing Biases 63
untested causal chains. Thus far, we have seen that children interacting with a depressed mother
show depressotypic mood and facial affect processing (Diego et al., 2004; Joormann et al., 2007)
and evidence of poor bonding and attachment (Boyd et al., 2006; Dawson, Klinger, Panagiotides,
Hill, & Spieker, 1992). Consistent with cognitive-interpersonal and interpersonal models,
insecure attachment, interpersonal difficulties, and insufficient social support have all been
linked to depression (Coyne, 1976; Gotlib & Hammen, 1992; Lewinsohn, 1974) and to abnormal
facial affect processing (Leppӓnen & Hietanen, 2001; Pollak et al., 2000). We have also noted
that gene-by-environment interactions may be associated with abnormal depressive neural
processing of affective faces (Wolfensberger et al., 2008). Specific dysfunction in the temporal
and orbitofrontal cortices has been linked to social impairments (Hornak et al., 2003; Rolls,
2008), and abnormal parietotemporal and orbitofrontal responses to depression-themed facial
affect have been found in depression (Cavanagh & Geisler, 2006; Deldin et al., 2001; Lee et al.,
2008). This, in addition to blunted neurocognitive-emotional response to happy faces (Domschke
et al., 2008; Surguladze et al., 2005), suggests that depressed individuals can experience a more
difficult, less positive social environment—also consistent with interpersonal theories. In turn,
abnormal neural responses to facial affect have been linked to cognitive biases in depression
(Dannlowski et al., 2007a).
Importantly, for depressed individuals, viewing others’ facial affect can elicit prolonged
depressed mood and negative automatic thoughts about the self in relation to others (Frewen &
Dozois, 2005; Persad & Polivy, 1993). However, we do not yet know whether premorbid
affective processing biases prospectively predict new onsets of depression. Armed with a
foundational network of interrelations, longitudinal research can help differentiate factors that
cause, maintain, or spuriously accompany depression. Although the relative absence of
Processing Biases 64
prospective studies is a limitation of the literature, the few studies that have examined facial
affect processing prospectively (Bouhuys et al., 1999; Bouhuys et al., 1996; Geerts & Bouhuys,
1998; Suslow et al., 2004) support the promising potential for future research.
Refined theory-testing should more comprehensively assess relationships between facial
affect processing and the reviewed depression models. To substantiate proposed interpersonal
mechanisms, empirical research will need to connect abnormal facial affect perception with
depressotypic reassurance seeking and social dysfunction. Perceptual-social links similar to those
found in normal, autistic, schizophrenic, and Williams syndrome groups (Greimel et al., 2010;
Meyer & Kurtz, 2009; Santos et al., 2009) might also be found in depression-prone groups.
Greater support for putative cognitive-interpersonal model processes would be provided by
research directly linking abnormal facial affect processing with negative thought patterns in
depression-vulnerable children. Regarding the social risk hypothesis, research should examine if
depressed individuals are more sensitive or attentive to disgust and contempt displays, as these
facial affects might reliably predict rejection (Fischer & Roseman, 2007; Rozin et al., 2008).
It will also be important to determine whether particular cognitive biases related to
emotional facial expressions are dependent on mood-state activation (i.e., occurring in currently
dysphoric or depressed individuals) or whether biases are also associated with trait-but-not-state
vulnerability status (e.g., nondysphoric formerly depressed individuals, nondysphoric children of
depressed parents) under certain conditions. For ease of description, we have assumed that all at-
risk groups may have similar cognitive processing biases. Clearly, this may not be the case.
Studies that are able to examine multiple at-risk groups—with and without mood priming
procedures—will be uniquely positioned to test this naïve assumption. Determining which biases
appear prior to a first onset or persist after depression remission will provide crucial knowledge
Processing Biases 65
about vulnerability processes. In turn, this could inform prevention efforts. For example,
paradigms that remediate negative emotional information processing biases are accumulating
empirical support (see Koster, Fox, & MacLeod, 2009 for introduction to cognitive bias
modification). It will be important to determine whether facial affect processing biases are also
mutable through psychosocial intervention.
Conclusion
Caveats, comments, and recommendations aside, neurocognitive research examining
facial affect processing provides moderate support for cognitive models of depression and
glimpses into how processing biases manifest in brain activity. In that facial affect processing
impacts attachment, social competence, and resource acquisition (Cozolino, 2002; Leppӓnen &
Hietanen, 2001; Wilson, 1999), it should be particularly important for those susceptible to
depression. These individuals might perceive and remember a more daunting social world, which
could promote dysphoria, self-deprecation, isolation, and rumination (Frewen & Dozois, 2005;
Persad & Polivy, 1993; Raes et al., 2006). Although the current literature implicates
interrelations among interpersonal experience, depressive cognition, and social behavior, future
work must directly test potential causes and effects of depressive facial affect processing.
Processing Biases 66
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Table 1
Studies evaluating identification of facial affect
Study
Participants Task Stimuli Presented Normal, General,
Specific Effects
Findings Description
Beevers et al.
(2009)
52 Dys, 55 HC
college students. AIT with
morphed
emotion dyadic
faces
Faces: sad, happy, angry,
fearful, morphed happy-
sad, happy-fearful, fearful-
angry, sad-angry Duration: response latency
N, S Dys = HC identification of
prototype affects. Dys > HC
identification of sadness mixed
with happiness, fear mixed with
happiness.
Bouhuys et al.
(1996)
30 CD, 1
cyclothymic, 2
dysthymic patients.
Emotion
recognition and
judgment task
Schematic faces: sad,
angry, disgusted, fearful,
happy, surprise Duration: response latency
N, S CD patients who perceived less
rejection, sadness, anger in
ambiguous faces had less
symptomatic improvement after 6
& 30 weeks.
Deveney &
Deldin (2004)
17 CD, 17 HC
community
individuals.
Identity
matching task
(yes/no) with
serially
presented face
pairs
Faces: sad, happy, neutral Duration: response latency
S In HC, slow wave ERP amplitudes
following sad < happy faces. In
CD, slow wave amplitudes
following sad = happy faces.
Frewen &
Dozois (2005)
105 college women
(48 nondysphoric, 55
dysphoric).
Timed emotion
recognition task Faces: angry, sad, fearful,
happy, disgusted, neutral Duration: up to 10,000 ms
N BDI scores not significantly
correlated with accuracy of affect
identification for any of the affects.
Gaebel &
Wolwer
(1992)
21 CD inpatients, 15
HC community
individuals.
AIT Faces: angry, happy, sad,
fearful, disgusted,
surprised Duration: 8,000 ms
N CD = HC performance on facial
affect identification.
Gollan et al.
(2008) 29 CD, 37 HC
individuals from
AIT with forced
choice among
Faces: happy, neutral, sad,
angry, disgusted, fearful S CD > HC bias of interpreting
neutral faces as sad. CD = HC
Processing Biases 97
university campus
and community. non-neutral
affects on
neutral
presentations
Duration: 3,000 ms accuracy across affects. CD > HC
reaction time to sad faces.
Gollan et al.
(2010)
44 CD, 44 HC
community
individuals.
AIT Faces: sad, harsh, surprise,
happy in 10-80%
gradations from neutral,
and 100% neutral faces Duration: 500 ms
S CD > HC in accuracy for labeling
sadness, collapsing across all affect
intensity levels. CD > HC between
10-40% sadness intensities. CD =
HC between 50-80% sadness
intensities.
Gur et al.
(1992)
14 CD, 14 HC clinic
and community
individuals.
Facial
discrimination
task
Faces: neutral, varying
degrees of happy-neutral,
sad-neutral. Stimuli
gender-matched to
participant Duration: 7,000 ms
S CD < HC on sensitivity to happy-
neutral faces, specificity for sad
faces. CD perceived happy as more
neutral, neutral as more sad. In
CD, higher negative affect
inversely correlated with accurate
identification of sad faces.
Hale (1998) 48 CD, 47 HC clinic
and community
individuals.
Emotion
recognition and
judgment task
Schematic faces: happy,
sad, fearful, rejecting,
angry, disgusted, inviting,
three ambiguous blends Duration: response latency
S CD > HC judging sadness in
prototype and ambiguous affective
faces. Increased judgment of
sadness at baseline related to
severity of symptoms, also to
persistence of depression at 13, 26
weeks.
Joormann &
Gotlib (2006)
23 CD, 27 SP, 26 HC.
Anxiety disorders
excluded from CD.
AIT with
morphed
emotion after
sad mood prime
Faces: neutral morphed
with happy, sad, angry,
fearful in fluidly
increasing affective
intensity Duration: 500 ms motion
picture style presentation
S CD required less intensity before
identifying sad compared to angry
affect. CD required greater
intensity before identifying happy
affect than HC, SP.
Processing Biases 98
LeMoult et al.
(2009)
39 PRD (3 with
current anxiety
disorders), 56 NC.
AIT with
morphed
emotion after
sad mood prime
Faces: neutral morphed
with happy, sad, angry in
fluidly increasing affective
intensity Duration: 500 ms motion
picture style presentation
S PRD required greater intensity
before identifying positive affect
than NC.
Leppӓnen et
al. (2004)
18 CD, 18 HC
individuals. Excluded
comorbid psychiatric
diagnoses.
AIT Faces: happy, sad, neutral Duration: 200 ms
N, S CD = HC at identifying sad, happy
faces. CD < HC accuracy in
identifying neutral, but no specific
bias.
Merens et al.
(2008)
18 individuals with
remitted MDD. Pre- and post-
ATD facial
recognition task
for sensitivity to
affective
intensities
Faces: happy, sad, angry,
disgust, fear in 10-100%
gradations from neutral.
S Accurate identification of sadness
pre-ATD predicted more severe
depressive symptoms post-ATD.
High-dose ATD associated with
impaired recognition and memory
of fear. Low-dose ATD related to
faster processing of disgust.
Milders et al.
(2010)
19 CD (5 CAD), 25
HC community
individuals.
Emotion
matching task,
emotion labeling
task
Faces: sad, happy, fearful,
disgusted, angry in 20-
80% gradations from
neutral Duration: 1000 or 2000 ms
S CD > HC in accuracy for labeling
sadness, collapsing across all affect
intensity levels. CD > HC at 20%
and 40% sadness intensities. CD =
HC at 60% and 80% sadness
intensities. CD = HC on matching
task.
Noreen &
Ridout (2010)
29 Dys, 22 HC
students. AIT, RMT for
affect displayed,
RMT for identity
displayed
Faces: happy, sad, angry,
neutral Duration: 2500 ms
N Dys = HC identifying affect. Dys
< HC on recognition memory
across affects and specific
identities that had previously
displayed angry, happy or neutral
affect. Controlled for anxiety.
Persad & 16 Dys and 16 HC Facial affective Faces: happy, sad, angry, G Dys and CD groups < HC college
Processing Biases 99
Polivy (1993) college students. 16
CD and 11
nondepressed
psychiatric patients.
booklet (emotion
labeling)
fearful, surprised,
contemptuous, disgusted,
indifferent Duration: response latency
students in accurate identification
across affects. No emotion-specific
deficit or bias in CD groups.
Ridout,
Noreen et al.
(2009)
Study 1: 24 Dys, 20
HC. Study 2: 24 negative
mood-induced HC,
24 positive mood-
induced HC.
Study 1: AIT
then RMT Study 2: Positive
or negative MI,
AIT, RMT
Faces: happy, neutral, sad Duration: response latency
for AIT and RMT.
S Study 1: Dys < HC identification
of sad and neutral faces, memory
for happy faces. Dys > HC
memory for sad. Dys memory for
sad > happy. Study 2: Negative MI HC >
positive MI HC in identification of
sad faces, memory for sad faces.
Negative MI HC identification of
sad > neutral, also memory for sad
> happy or neutral.
Suslow et al.
(2004)
22 CD (11 CAD, 11
no comorbidity)
treatment-seeking, 22
HC community
persons.
Face-in-the-
crowd. Before
and after 6
weeks
psychotherapy
Faces: schematic positive,
neutral, negative Duration: 500 ms
S, N CAD > HC on RT detection of
positive faces. MDD without
comorbidity = HC.
Surguladze et
al. (2004)
27 CD, 29 HC
individuals from
inpatient/outpatient
clinics and
community.
AIT Faces: 100% happy, sad,
prototypes and 50%
happy-neutral, sad-neutral
morphs Duration: 100 ms, 2000
ms
G, N, S On 100 ms sad and happy affects,
CD < HC accuracy. On 2,000 ms
prototype affects CD = HC. On
2,000 ms affect morphs, CD bias to
interpret happy as neutral, HC bias
to interpret neutral as happy. In
CD, higher depression scores
associated with worse
identification of sad affect.
Weniger et al.
(2004)
21 CD, 30 HC
inpatient clinic and
community
Emotion
matching task,
emotion arousal
Faces: angry, sad, fearful,
happy, disgusted Duration: response latency
G CD < HC overall matching
performance. CD < HC in rated
arousal intensity for emotional
Processing Biases 100
.individuals rating faces.
Wright et al.
(2009)
79 CD (56 women,
15 CAD), 72 HC (34
women) clinic and
college community
individuals.
AIT Faces: happy, sad, angry,
fearful Duration: 300 ms
S, N CD women < HC women in
accurate identification of sad,
fearful faces. Misinterpreted
fearful as angry. No main effects
of depression or gender. No
differences between CD, HC men.
Yoon et al.
(2009) 21 CD, 23 SAD, 20
HC community
individuals.
Forced choice
intensity
judgment task
(presentations of
paired faces with
differing affects)
Faces: happy, sad, angry,
fearful faces of 40%
affective intensity and
neutral faces Duration: response latency
S CD < HC, SAD in proportional
likelihood of judging happy faces
as more intense than neutral faces.
CD, SAD < HC in likelihood of
judging happy faces as more
intense than sad or angry faces.
Note. AIT = affect identification task; ATD = acute tryptophan depletion; BDI = Beck Depression Inventory; CAD = comorbid anxiety and
depression; CD = currently depressed; Dys = Dysphoric; ERP = event-related potentials; FMT = face memory task; FD = formerly depressed; HC
= healthy controls; MDD = major depressive disorder; MI = mood induction; ND = never depressed; RMT = recognition memory test; RT =
reaction time; SAD = social anxiety disorder; SP = social phobia.
Processing Biases 101
Table 2
Studies evaluating selective attention to affective faces
Study
Participants Task Stimuli Presented Bias Found Findings Description
Bradley et al.
(1998)
20 high, 20 low trait
anxiety students.
Dot-probe Faces: happy,
threatening, neutral Duration: 500 ms, 1250
ms
↓ happy Dys correlated with reduced attention to
happy faces for 500 ms but not 1250 ms
presentations.
Bradley et al.
(2000)
55 students, mostly
high and low state
anxiety.
Dot-probe Faces: happy, sad,
threatening, neutral
Duration: 500 ms
↓ happy Dys correlated with reduced attention to
happy faces. No bias toward sad faces.
Cavanagh &
Geisler (2006)
18 CD (no SCID
diagnosis), 18 HC
students.
Oddball Faces: happy, fearful
(targets) neutral
(standard) Duration: 750 ms
↓ happy CD < HC P300 amplitude for happy
faces only. CD > HC P300 latency for happy faces
only.
Fritzsche et al.
(2009)
20 CD, 20 FD, 20 NC,
20 ND with diagnosed
asthma. Past/present
anxiety disorders
excluded.
Dot-probe Faces: sad, happy,
neutral Duration: 1000 ms
↓ happy, ↑ sad CD showed bias toward sad, away from
happy faces. FD > HC attention to sad.
HC showed bias toward happy, away
from sad faces.
Gibb et al.
(2009) 40 children (10 FD) of
mothers with past
MDD within child’s
lifetime, 32 children of
NC mothers.
Dot-probe Faces: sad, happy,
angry, neutral Duration: 1000 ms
↓ sad Children of FD mothers showed specific
bias away from sad faces. Finding
strongest in carriers of 5-HTTLPR S or
LG allele.
Goeleven et al.
(2006)
20 CD, 20 FD, 20 HC
community individuals. Negative
affective
priming
Faces: happy, sad Duration: response
latency
↑ sad CD < HC, FD on NAP effect for sad
faces. FD lacked NAP effect for both
valences.
Processing Biases 102
Gotlib, Kasch
et al. (2004)
88 CD, 35 GSP, 55 ND
clinic and community
individuals. Anxiety
disorders excluded
from CD.
Dot-probe Faces: sad, happy,
angry, neutral Duration: 1000 ms
↑ sad CD bias toward sad faces. Positive
correlation between depressive
symptoms and bias away from happy
faces. No biases in comparison groups.
Gotlib,
Krasnoperova
et al. (2004)
19 CD, 18 GAD, 16
HC individuals from
clinic and community. Anxiety disorders
excluded from CD.
Dot-probe Faces: sad, happy,
angry, neutral Duration: 1000 ms
↑ sad CD bias toward sad faces compared to
happy or angry faces. No biases in
comparison groups.
Hsieh & Ko
(2004)
17 high, 13 low trait
depression students. DOAT, RMT Faces: happy, angry,
sad, neutral Duration: 300, 400, 500
ms randomly varied
↑ sad DOAT: High group > low group
attention to sad faces. RMT: High group > low group on sad
faces.
Joormann &
Gotlib (2007)
26 CD, 23 FD, and 19
NC clinic and
community individuals.
Anxiety disorders
excluded.
Dot-probe Faces: happy, neutral,
sad Duration: 1000 ms
↑ sad CD, FD bias toward sad faces. HC
avoided sad faces, oriented to happy
faces.
Joorman et al.
(2007)
21 girls of mothers with
history of RMD, 20
girls of NC mothers
from clinic and
community.
Dot-probe
following sad
MI
Faces: happy, neutral,
sad. Duration: 1500 ms
↑ sad At-risk girls bias toward sad faces, not
happy faces. Low-risk daughters bias
toward happy faces, not sad faces.
Leyman et al.
(2007)
20 CD, 20 HC
individuals from clinic
and governmental
agency.
Exogenous
cueing task Faces: angry, neutral Duration: 1000 ms
↑ angry CD > HC attentional engagement with
angry faces. CD attentional engagement
for angry > neutral faces.
Mogg et al.
(2000) 15 CD (13 CAD), 16
HC individuals from
Dot-probe Faces: happy, sad,
threatening, neutral none CD = HC for dot-probe RT and eye
movement monitoring.
Processing Biases 103
community.
Duration: 1000 ms
Suslow et al.
(2001)
15 mood disordered (10
MDD, 5 dysthymia)
treatment seeking
persons, 15 HC
community persons.
Face-in-the-
crowd Schematic faces:
positive, neutral,
negative Duration: 500 ms
↓ happy Mood disordered group RT > HC RT for
positive faces only.
Wells et al.
(2010)
32 Dys, 24 HC college
students. Eye tracking
during passive
viewing trials
of 12s, RMT
Faces: happy, sad,
angry, neutral Duration: 12000 ms
↑ angry Dys > HC distance between attentional
fixations on angry faces. Dys > HC in
recognition accuracy for angry faces,
mediated by fixation effect. Note. 5-HTTLPR = serotonin transporter-linked polymorphic region; CAD = comorbid anxiety and depression; CD = currently depressed; DOAT
= deployment of attention task; Dys = Dysphoric; FMT = face memory task; FD = formerly depressed; GAD = generalized anxiety disorder; GSP
= generalized social phobia; HC = healthy controls; MDD = major depressive disorder; MI = mood induction; NAP = negative affective priming;
NC = never disordered controls; ND = never depressed; P300 = third positive peak in event-related electroencephalographic waveform; PRD =
past recurrent depression; RMD = recurrent major depression; RMT = recognition memory test; RT = reaction time; SCID = Structured Clinical
Interview for DSM-IV.
Processing Biases 104
Table 3
Studies evaluating memory for affective faces
Study
Participants Task Stimuli Presented Bias Found Findings
Deldin et al.
2001
19 CD (14 inpatient, 5
community), 15 HC
from community.
Positive/negative
affective judgment,
RMT
Faces: happy, sad,
neutral Duration: 200 ms
none For HC, encoding P300 greater,
recognition memory P300 lower
for positive faces. For CD, no
differences. CD = HC in memory
accuracy and RT.
Gilboa-
Schechtman
et al. (2002)
23 CAD, 20 ANX
treatment seeking and
23 HC community
individuals.
Incidental encoding,
FMT Faces: sad, angry,
happy, and neutral Duration: response
latency
↑ sad, ↑ angry CAD recall: sad and angry faces >
happy and neutral faces. HC
recall: happy and neutral faces >
sad and angry faces.
Hsieh & Ko
(2004)
17 high, 13 low trait
depression students. DOAT, RMT Faces: happy, angry,
sad, neutral Duration: 300, 400, 500
ms randomly varied
↑ sad DOAT: High group > low group
attention to sad faces. RMT: High group > low group on
sad faces.
Jermann et al.
(2008)
65 Dys, 78 HC
students and
community
individuals.
Intentional encoding,
remember/know/guess
recognition memory
procedure
Faces: happy, neutral,
sad Duration: 5000 ms for
encoding task, response
latency for memory task
↑ sad Dys > HC recognition memory
for sad faces.
Noreen &
Ridout (2010)
29 Dys, 22 HC
medication-free
students.
AIT, RMT affect
discrimination trials
15s post-display,
RMT identity
discrimination trials
15s post-display
Faces: happy, sad,
angry, neutral Duration: 2500 ms
↓ happy Dys = HC identifying affect. Dys
> HC on WM across affects. In
Dys, WM for identities that
previously displayed happy <
those that displayed angry, happy
or neutral affect.
Pine et al.
(2004) Adolescent children:
19 FD, 26 of NC
FMT Faces: happy, fearful,
angry ↓ fearful FD children reduced memory for
fearful faces. No biases for
Processing Biases 105
parents, 53 of FD
parents, 58 of CAD
parents.
Duration: 4000 ms
children of parents with history of
depression.
Ridout et al.
(2003)
16 CD, 16 HC
community persons. RMT Faces: happy, sad,
neutral Duration:
response latency
↑ sad CD recognized sad > neutral >
happy. HC recognized happy >
neutral > sad.
Ridout,
Dritschel et
al. (2009)
16 CD, 18 HC clinic
and community
individuals.
Implicit encoding
(gender identification)
task, RMT
Faces: happy, neutral,
sad Duration: response
latency for encoding and
recognition phases
none CD showed no recognition
memory bias for sad faces. CD,
HC show no significant memory
differences for specific valences.
Ridout,
Noreen et al.
(2009)
Study 1: 24 Dys, 20
HC Study 2: 24 negative
mood-induced HC, 24
positive mood-induced
HC.
Study 1: AIT then
RMT Study 2: Positive or
negative MI, AIT,
RMT
Faces: happy, neutral,
sad Duration: response
latency for AIT and
RMT.
↑ sad Study 1: Dys < HC identification
of sad and neutral faces, memory
for happy faces. Dys > HC
memory for sad. Dys memory for
sad > happy. Study 2: Negative MI HC >
positive MI HC in identification
of sad faces, memory for sad
faces. Negative MI HC
identification of sad > neutral,
also memory for sad > happy or
neutral.
Wells et al.
(2010)
32 Dys, 24 HC college
students. Eye tracking during
focused viewing,
RMT
Faces: happy, sad,
angry, neutral Duration: 12000 ms
↑ angry Dys > HC distance between
attentional fixations on angry
faces. Dys > HC in recognition
accuracy for angry faces,
mediated by fixation effect.
Note. AIT = affect identification task; ANX = anxiety disorder; CAD = comorbid anxiety and depression; CD = currently depressed; DOAT =
deployment of attention task; Dys = Dysphoric; FMT = face memory task; FD = formerly depressed; GAD = generalized anxiety disorder; HC =
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